From 2f2017815f13199a7efc6a6cc8d6d6d7aeb214f5 Mon Sep 17 00:00:00 2001
From: leonhardhennig <leonhardhennig@users.noreply.github.com>
Date: Mon, 8 Jan 2024 11:02:50 +0000
Subject: [PATCH] deploy: 696019b34d4daca3d329999a074b226aac031257

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diff --git a/authors/amy-siu/index.html b/authors/amy-siu/index.html
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diff --git a/authors/amy-siu/index.xml b/authors/amy-siu/index.xml
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diff --git a/authors/aurelie-neveol/index.xml b/authors/aurelie-neveol/index.xml
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diff --git a/authors/berkay-kopru/index.xml b/authors/berkay-kopru/index.xml
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diff --git a/authors/bhuvanesh-verma/index.xml b/authors/bhuvanesh-verma/index.xml
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diff --git a/authors/carol-anderson/index.xml b/authors/carol-anderson/index.xml
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diff --git a/authors/cennet-oguz/index.xml b/authors/cennet-oguz/index.xml
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diff --git a/authors/chao-wang/index.xml b/authors/chao-wang/index.xml
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diff --git a/authors/chen-kai-wang/index.html b/authors/chen-kai-wang/index.html
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diff --git a/authors/chen-kai-wang/index.xml b/authors/chen-kai-wang/index.xml
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diff --git a/authors/christel-gerardin/index.html b/authors/christel-gerardin/index.html
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diff --git a/authors/christel-gerardin/index.xml b/authors/christel-gerardin/index.xml
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diff --git a/authors/christoph-alt/index.html b/authors/christoph-alt/index.html
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+++ b/authors/christoph-alt/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><section id=profile-page class=pt-5><div class=container><div class=row><div class="col-12 col-lg-4"><div id=profile><img class="avatar avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"><div class=portrait-title><h2>Christoph Alt</h2><h3><span></span></h3></div><ul class=network-icon aria-hidden=true><li><a href=../../#contact aria-label=envelope><i class="fas fa-envelope big-icon"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener aria-label=linkedin><i class="fab fa-linkedin big-icon"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener aria-label=google-scholar><i class="ai ai-google-scholar big-icon"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener aria-label=github><i class="fab fa-github big-icon"></i></a></li></ul></div></div><div class="col-12 col-lg-8"><div class=article-style><p>Christoph is now a postdoc in the <a href=https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/ml target=_blank rel=noopener>Machine Learning group</a> at Humboldt University of Berlin.</p></div><div class=row></div></div></div><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/acl2022-repl4nlp-chen-fewie/>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</a></li><li><a href=../../publication/acl2022-nlppower-harbecke-f1/>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/deeplee/>DEEPLEE</a></li><li><a href=../../project/plass/>PLASS</a></li><li><a href=../../publication/semeval2020-huebner-defx/>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</a></li><li><a href=../../publication/acl-ecnlp2020-zhang-bootstrapping/>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</a></li><li><a href=../../publication/acl-srw2020-harbecke-considering/>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</a></li><li><a href=../../publication/acl2020-alt-probing/>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</a></li><li><a href=../../publication/acl2020-alt-tacred-revisited/>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</a></li><li><a href=../../publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</a></li><li><a href=../../publication/acl2019-alt-finetuning/>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</a></li><li><a href=../../publication/akbc2019-alt-improving/>Improving Relation Extraction by Pre-Trained Language Representations</a></li><li><a href=../../publication/mex-an-information-extraction-platform-for-german-medical-text/>mEx - an Information Extraction Platform for German Medical Text</a></li><li><a href=../../publication/learning-explanations-from-language-data/>Learning Explanations From Language Data</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/christoph-alt/index.xml b/authors/christoph-alt/index.xml
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Christoph Alt | DFKI-NLP</title><link>https://dfki-nlp.github.io/authors/christoph-alt/</link><atom:link href="https://dfki-nlp.github.io/authors/christoph-alt/index.xml" rel="self" type="application/rss+xml"/><description>Christoph Alt</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Thu, 26 May 2022 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg</url><title>Christoph Alt</title><link>https://dfki-nlp.github.io/authors/christoph-alt/</link></image><item><title>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</title><link>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</guid><description/></item><item><title>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</title><link>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</guid><description/></item><item><title>Cora4NLP</title><link>https://dfki-nlp.github.io/project/cora4nlp/</link><pubDate>Tue, 23 Feb 2021 11:23:02 +0100</pubDate><guid>https://dfki-nlp.github.io/project/cora4nlp/</guid><description/></item><item><title>DEEPLEE</title><link>https://dfki-nlp.github.io/project/deeplee/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/deeplee/</guid><description/></item><item><title>PLASS</title><link>https://dfki-nlp.github.io/project/plass/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/plass/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. 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diff --git a/authors/christopher-ebert/index.xml b/authors/christopher-ebert/index.xml
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diff --git a/authors/cristian-grozea/index.xml b/authors/cristian-grozea/index.xml
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diff --git a/authors/danilo-schmidt/index.html b/authors/danilo-schmidt/index.html
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diff --git a/authors/danilo-schmidt/index.xml b/authors/danilo-schmidt/index.xml
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Darryl Johan Estrada</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/wmt-emnlp2022-roller-findings/>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/darryl-johan-estrada/index.xml b/authors/darryl-johan-estrada/index.xml
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diff --git a/authors/david-samhammer/index.html b/authors/david-samhammer/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>David Samhammer</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/samhammer-2023-klinische/>Klinische Entscheidungsfindung mit Künstlicher Intelligenz: Ein interdisziplinärer Governance-Ansatz</a></li><li><a href=../../publication/plosone2023-roller-performance/>When performance is not enough—A multidisciplinary view on clinical decision support</a></li><li><a href=../../publication/frontiers2022-roller-nothing/>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/david-samhammer/index.xml b/authors/david-samhammer/index.xml
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diff --git a/authors/dele-zhu/index.xml b/authors/dele-zhu/index.xml
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><section id=profile-page class=pt-5><div class=container><div class=row><div class="col-12 col-lg-4"><div id=profile><img class="avatar avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP><div class=portrait-title><h2>DFKI-NLP</h2><h3>Research Group at DFKI</h3><h3><a href=https://www.dfki.de/ target=_blank rel=noopener><span>German Research Center for Artificial Intelligence (DFKI)</span></a></h3></div><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de aria-label=envelope><i class="fas fa-envelope big-icon"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener aria-label=github><i class="fab fa-github big-icon"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener aria-label=grin><i class="fas fa-grin big-icon"></i></a></li></ul></div></div><div class="col-12 col-lg-8"><div class=article-style><p>DFKI-NLP is a Natural Language Processing group of researchers, software engineers and students at the Berlin office of the German Research Center for Artificial Intelligence (DFKI) working on basic and applied research in areas covering, among others, information extraction, knowledge base population, dialogue, sentiment analysis, and summarization. We are particularly interested in core research on learning in low-resource settings, reasoning over larger contexts, and continual learning. We strive for a deeper understanding of human language and thinking, with the goal of developing novel methods for processing and generating human language text, speech, and knowledge. An important part of our work is the creation of corpora, the evaluation of NLP datasets and tasks, and the explainability of (neural) models.</p><p>Key topics:</p><ul><li>Applied / domain-specific information extraction</li><li>Learning in low-resource settings and over large contexts</li><li>Construction and analysis of IE datasets, linguistic annotation</li><li>Multilingual information extraction</li><li>Evaluation methodology research</li><li>Explainability</li></ul><p>Our group forms a part of DFKI&rsquo;s <a href=https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology/ target=_blank rel=noopener>Speech and Language Technology</a> department led by Prof. Sebastian Möller, and closely collaborates with e.g. the <a href=https://www.tu.berlin/en/ target=_blank rel=noopener>Technische Universität Berlin</a>, DFKI&rsquo;s <a href=https://www.dfki.de/en/web/research/research-departments/multilinguality-and-language-technology/ target=_blank rel=noopener>Language Technology and Multilinguality</a> department and DFKI&rsquo;s <a href=https://www.dfki.de/en/web/research/research-departments/intelligent-analytics-for-massive-data/ target=_blank rel=noopener>Intelligent Analytics for Massive Data</a> group.</p></div><div class=row></div></div></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/federica-vezzani/index.xml b/authors/federica-vezzani/index.xml
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diff --git a/authors/gabriele-sarti/index.xml b/authors/gabriele-sarti/index.xml
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diff --git a/authors/georg-rehm/index.html b/authors/georg-rehm/index.html
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diff --git a/authors/georg-rehm/index.xml b/authors/georg-rehm/index.xml
index d06338e3..74e34163 100644
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diff --git a/authors/joao-f-silva/index.xml b/authors/joao-f-silva/index.xml
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diff --git a/authors/jonas-mikkelsen/index.xml b/authors/jonas-mikkelsen/index.xml
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diff --git a/authors/juan-m-banda/index.xml b/authors/juan-m-banda/index.xml
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diff --git a/authors/lana-yeganova/index.html b/authors/lana-yeganova/index.html
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diff --git a/authors/lana-yeganova/index.xml b/authors/lana-yeganova/index.xml
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diff --git a/authors/laura-seiffe/index.html b/authors/laura-seiffe/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Laura Seiffe</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/lrec2022-roller/>An Annotated Corpus of Textual Explanations for Clinical Decision Support</a></li><li><a href=../../publication/lrec2022-seiffe-subjective/>Subjective Text Complexity Assessment for German</a></li><li><a href=../../publication/mex-an-information-extraction-platform-for-german-medical-text/>mEx - an Information Extraction Platform for German Medical Text</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/laura-seiffe/index.xml b/authors/laura-seiffe/index.xml
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diff --git a/authors/leonhard-hennig/index.html b/authors/leonhard-hennig/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><section id=profile-page class=pt-5><div class=container><div class=row><div class="col-12 col-lg-4"><div id=profile><img class="avatar avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"><div class=portrait-title><h2>Leonhard Hennig</h2><h3>Senior Researcher</h3><h3><a href=https://www.dfki.de target=_blank rel=noopener><span>DFKI</span></a></h3></div><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de aria-label=envelope><i class="fas fa-envelope big-icon"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener aria-label=google-scholar><i class="ai ai-google-scholar big-icon"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener aria-label=github><i class="fab fa-github big-icon"></i></a></li></ul></div></div><div class="col-12 col-lg-8"><div class=article-style><p>I&rsquo;m a senior researcher and project manager at the DFKI Speech & Language Technology Lab. I&rsquo;m interested in applying machine learning techniques to computational linguistics problems, such as information extraction and summarization, and making these work on real-world, domain-specific, noisy data in low-resource settings, where little or no language resources are readily available. As a project lead, I&rsquo;ve managed various national research projects, such as Smart Data Web, PLASS, DAYSTREAM, and the DFKI part in the Berlin Big Data Center, as well as industry-funded projects, e.g. for Deutsche Telekom and Lenovo.</p></div><div class=row><div class=col-md-5><div class=section-subheading>Interests</div><ul class=ul-interests><li>Information Extraction</li><li>Summarization</li><li>Low-Resource Learning</li></ul></div></div></div></div><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/konvens2023-binsumait-etal-factuality/>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</a></li><li><a href=../../publication/acl2023-feldhus-smv/>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</a></li><li><a href=../../publication/acl2023-hennig-multitacred/>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</a></li><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../publication/emnlp2022-chen-meffiprompt/>Multilingual Relation Classification via Efficient and Effective Prompting</a></li><li><a href=../../publication/wiesp2022-binder-etal-full/>Full-Text Argumentation Mining on Scientific Publications</a></li><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../publication/acl2022-repl4nlp-chen-fewie/>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</a></li><li><a href=../../publication/acl2022-nlppower-harbecke-f1/>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../publication/konvens2021-hennig-mobie/>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/bbdc2/>BBDC2</a></li><li><a href=../../project/plass/>PLASS</a></li><li><a href=../../publication/semeval2020-huebner-defx/>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</a></li><li><a href=../../publication/acl-ecnlp2020-zhang-bootstrapping/>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</a></li><li><a href=../../publication/acl2020-alt-probing/>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</a></li><li><a href=../../publication/acl2020-alt-tacred-revisited/>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</a></li><li><a href=../../publication/lrec2020-aksenov-abstractive/>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</a></li><li><a href=../../publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</a></li><li><a href=../../publication/acl2019-alt-finetuning/>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</a></li><li><a href=../../publication/qomex2019-iskender-crowd/>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</a></li><li><a href=../../publication/akbc2019-alt-improving/>Improving Relation Extraction by Pre-Trained Language Representations</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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diff --git a/authors/leonhard-hennig/index.xml b/authors/leonhard-hennig/index.xml
index 8ac3b872..b1f81bac 100644
--- a/authors/leonhard-hennig/index.xml
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Leonhard Hennig | DFKI-NLP</title><link>https://dfki-nlp.github.io/authors/leonhard-hennig/</link><atom:link href="https://dfki-nlp.github.io/authors/leonhard-hennig/index.xml" rel="self" type="application/rss+xml"/><description>Leonhard Hennig</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Thu, 17 Aug 2023 08:33:03 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png</url><title>Leonhard Hennig</title><link>https://dfki-nlp.github.io/authors/leonhard-hennig/</link></image><item><title>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</title><link>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</link><pubDate>Thu, 17 Aug 2023 08:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</guid><description/></item><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</title><link>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</guid><description/></item><item><title>Data4Transparency</title><link>https://dfki-nlp.github.io/project/data4transparency/</link><pubDate>Sat, 01 Apr 2023 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/data4transparency/</guid><description/></item><item><title>Multilingual Relation Classification via Efficient and Effective Prompting</title><link>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</link><pubDate>Sat, 22 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</guid><description/></item><item><title>Full-Text Argumentation Mining on Scientific Publications</title><link>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</link><pubDate>Fri, 21 Oct 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</guid><description/></item><item><title>Text2Tech</title><link>https://dfki-nlp.github.io/project/text2tech/</link><pubDate>Wed, 01 Jun 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/text2tech/</guid><description/></item><item><title>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</title><link>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</guid><description/></item><item><title>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</title><link>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</guid><description/></item><item><title>BIFOLD</title><link>https://dfki-nlp.github.io/project/bifold/</link><pubDate>Thu, 20 Jan 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/bifold/</guid><description/></item><item><title>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</link><pubDate>Tue, 07 Sep 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</guid><description/></item><item><title>Cora4NLP</title><link>https://dfki-nlp.github.io/project/cora4nlp/</link><pubDate>Tue, 23 Feb 2021 11:23:02 +0100</pubDate><guid>https://dfki-nlp.github.io/project/cora4nlp/</guid><description/></item><item><title>BBDC2</title><link>https://dfki-nlp.github.io/project/bbdc2/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/bbdc2/</guid><description/></item><item><title>PLASS</title><link>https://dfki-nlp.github.io/project/plass/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/plass/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</title><link>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</guid><description/></item><item><title>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</guid><description/></item><item><title>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</link><pubDate>Mon, 29 Jul 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</guid><description/></item><item><title>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</title><link>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</link><pubDate>Mon, 24 Jun 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</guid><description/></item><item><title>Improving Relation Extraction by Pre-Trained Language Representations</title><link>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</link><pubDate>Mon, 20 May 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</guid><description/></item></channel></rss>
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Leonhard Hennig | DFKI-NLP</title><link>https://dfki-nlp.github.io/authors/leonhard-hennig/</link><atom:link href="https://dfki-nlp.github.io/authors/leonhard-hennig/index.xml" rel="self" type="application/rss+xml"/><description>Leonhard Hennig</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Thu, 17 Aug 2023 08:33:03 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png</url><title>Leonhard Hennig</title><link>https://dfki-nlp.github.io/authors/leonhard-hennig/</link></image><item><title>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</title><link>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</link><pubDate>Thu, 17 Aug 2023 08:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</guid><description/></item><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</title><link>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</guid><description/></item><item><title>Data4Transparency</title><link>https://dfki-nlp.github.io/project/data4transparency/</link><pubDate>Sat, 01 Apr 2023 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/data4transparency/</guid><description/></item><item><title>Multilingual Relation Classification via Efficient and Effective Prompting</title><link>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</link><pubDate>Sat, 22 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</guid><description/></item><item><title>Full-Text Argumentation Mining on Scientific Publications</title><link>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</link><pubDate>Fri, 21 Oct 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</guid><description/></item><item><title>Text2Tech</title><link>https://dfki-nlp.github.io/project/text2tech/</link><pubDate>Wed, 01 Jun 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/text2tech/</guid><description/></item><item><title>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</title><link>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</guid><description/></item><item><title>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</title><link>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</guid><description/></item><item><title>BIFOLD</title><link>https://dfki-nlp.github.io/project/bifold/</link><pubDate>Thu, 20 Jan 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/bifold/</guid><description/></item><item><title>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</link><pubDate>Tue, 07 Sep 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</guid><description/></item><item><title>Cora4NLP</title><link>https://dfki-nlp.github.io/project/cora4nlp/</link><pubDate>Tue, 23 Feb 2021 11:23:02 +0100</pubDate><guid>https://dfki-nlp.github.io/project/cora4nlp/</guid><description/></item><item><title>BBDC2</title><link>https://dfki-nlp.github.io/project/bbdc2/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/bbdc2/</guid><description/></item><item><title>PLASS</title><link>https://dfki-nlp.github.io/project/plass/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/plass/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</title><link>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</guid><description/></item><item><title>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</guid><description/></item><item><title>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</link><pubDate>Mon, 29 Jul 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</guid><description/></item><item><title>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</title><link>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</link><pubDate>Mon, 24 Jun 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</guid><description/></item><item><title>Improving Relation Extraction by Pre-Trained Language Representations</title><link>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</link><pubDate>Mon, 20 May 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</guid><description/></item></channel></rss>
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diff --git a/authors/lisa-raithel/index.html b/authors/lisa-raithel/index.html
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--- a/authors/lisa-raithel/index.html
+++ b/authors/lisa-raithel/index.html
@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><section id=profile-page class=pt-5><div class=container><div class=row><div class="col-12 col-lg-4"><div id=profile><img class="avatar avatar-circle" src=../../authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt="Lisa Raithel"><div class=portrait-title><h2>Lisa Raithel</h2><h3>PhD Candidate</h3><h3><a href=https://www.dfki.de target=_blank rel=noopener><span>DFKI</span></a></h3><h3><a href=https://www.lisn.upsaclay.fr/ target=_blank rel=noopener><span>LISN</span></a></h3><h3><a href=https://www.tu.berlin/ target=_blank rel=noopener><span>Technische Universität Berlin</span></a></h3><h3><a href=https://www.universite-paris-saclay.fr/ target=_blank rel=noopener><span>Université Paris-Saclay</span></a></h3></div><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de aria-label=envelope><i class="fas fa-envelope big-icon"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener aria-label=google-scholar><i class="ai ai-google-scholar big-icon"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener aria-label=github><i class="fab fa-github big-icon"></i></a></li><li><a href=https://www.linkedin.com/in/lisa-raithel-3b2865240/ target=_blank rel=noopener aria-label=linkedin><i class="fab fa-linkedin big-icon"></i></a></li></ul></div></div><div class="col-12 col-lg-8"><div class=article-style><p>Hey!</p><p>I&rsquo;m Lisa, a PhD candidate at both <a href=https://www.tu.berlin/ target=_blank rel=noopener>Technical University Berlin</a> and <a href=https://www.universite-paris-saclay.fr/ target=_blank rel=noopener>Université Paris-Saclay</a>. My supervisors are <a href=https://perso.limsi.fr/pz/ target=_blank rel=noopener>Pierre Zweigenbaum</a> and <a href=https://www.tu.berlin/en/qu/about-us/management/ target=_blank rel=noopener>Sebastian Möller</a>, and I work closely with <a href=https://dfki-nlp.github.io/authors/philippe-thomas/ target=_blank rel=noopener>Philippe Thomas</a> and <a href=https://dfki-nlp.github.io/authors/roland-roller/ target=_blank rel=noopener>Roland Roller</a>.
 Currently, I am staying at <a href=https://www.lisn.upsaclay.fr/ target=_blank rel=noopener>LISN</a> in France, after that I will move back to Berlin, Germany.</p><p>I am a <a href=https://www.uni-potsdam.de/en/ling/index target=_blank rel=noopener>Computational Linguist</a> (Potsdam University) and received a MSc. in <a href=https://www.ling.uni-potsdam.de/cogsys/about.html target=_blank rel=noopener>Cognitive Systems</a> (also Potsdam).</p><p>My thesis is embedded in the trilateral project <a href="https://keepha.lisn.upsaclay.fr/wiki/doku.php?id=start" target=_blank rel=noopener>KEEPHA</a> where I am researching the possibilities to detect and extract Adverse Drug Reactions across languages.</p><p>I am mostly interested in cross-lingual learning and NLP in health scenarios.</p></div><div class=row></div></div></div><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/lrec2022-raithel-cross-adr/>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</a></li><li><a href=../../publication/cl-nvc/>Cross-lingual Neural Vector Conceptualization</a></li><li><a href=../../publication/nvc/>Neural Vector Conceptualization for Word Vector Space Interpretation</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/authors/lisa-raithel/index.xml b/authors/lisa-raithel/index.xml
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diff --git a/authors/ludwig-sickert/index.html b/authors/ludwig-sickert/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Ludwig Sickert</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/acl2023-sarti-inseq/>Inseq: An Interpretability Toolkit for Sequence Generation Models</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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diff --git a/authors/ludwig-sickert/index.xml b/authors/ludwig-sickert/index.xml
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diff --git a/authors/luis-alberto-robles-hernandez/index.html b/authors/luis-alberto-robles-hernandez/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Luis Alberto Robles Hernandez</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/database2023-roller-biocreative/>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/luis-alberto-robles-hernandez/index.xml b/authors/luis-alberto-robles-hernandez/index.xml
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diff --git a/authors/maika-vicente-navarro/index.html b/authors/maika-vicente-navarro/index.html
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@@ -3,7 +3,7 @@
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Maika Vicente Navarro</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/wmt-emnlp2022-roller-findings/>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/maika-vicente-navarro/index.xml b/authors/maika-vicente-navarro/index.xml
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diff --git a/authors/malte-ostendorff/index.html b/authors/malte-ostendorff/index.html
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@@ -8,7 +8,7 @@
 Due to the large amounts of available data, all my work requires the use of scalable and distributed computing. Generally speaking, all topics that are somehow related to the following fields can be considered as my research interest:</p><ul><li>Recommender Systems</li><li>Natural Language Processing</li><li>Text Mining</li><li>Applied Machine Learning</li><li>Scalable Data Processing (&ldquo;Big Data&rdquo;)</li><li>Legal Tech</li><li>Language Models</li></ul><p>Feel free to contact me if you have any questions regarding my work.
 I am always open for new ideas, projects and collaborations with other researchers and students.</p></div><div class=row></div></div></div><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/pml4dc-2023-ostendorff/>Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning</a></li><li><a href=../../publication/emnlp2022-ostendorff/>Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings</a></li><li><a href=../../publication/lrec2022-calizzano/>Generating Extended and Multilingual Summaries with Pre-trained Transformers</a></li><li><a href=../../publication/lrec2022-dehio/>Claim Extraction and Law Matching for COVID-19-related Legislation</a></li><li><a href=../../publication/jcdl2022-ostendorff/>Specialized Document Embeddings for Aspect-based Similarity of Research Papers</a></li><li><a href=../../publication/acl2022-ruan/>HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information</a></li><li><a href=../../publication/ostendorff2021/>Evaluating Document Representations for Content-based Legal Literature Recommendations</a></li><li><a href=../../publication/ostendorff2020c/>Aspect-based Document Similarity for Research Papers</a></li><li><a href=../../publication/ostendorff2020/>Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles</a></li><li><a href=../../publication/ostendorff2019/>Enriching BERT with Knowledge Graph Embedding for Document Classification</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/authors/malte-ostendorff/index.xml b/authors/malte-ostendorff/index.xml
index 2dc3dcbb..3f869a9c 100644
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diff --git a/authors/malvina-nissim/index.xml b/authors/malvina-nissim/index.xml
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diff --git a/authors/manuel-mayrdorfer/index.html b/authors/manuel-mayrdorfer/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Manuel Mayrdorfer</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/plosone2023-roller-performance/>When performance is not enough—A multidisciplinary view on clinical decision support</a></li><li><a href=../../publication/frontiers2022-roller-nothing/>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</a></li><li><a href=../../publication/frontiers2022-roller-evaluation/>Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/manuel-mayrdorfer/index.xml b/authors/manuel-mayrdorfer/index.xml
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diff --git a/authors/marc-hubner/index.html b/authors/marc-hubner/index.html
index 483930d2..ed2e763a 100644
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+++ b/authors/marc-hubner/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Marc Hübner</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/semeval2020-huebner-defx/>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</a></li><li><a href=../../publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</a></li><li><a href=../../publication/acl2019-alt-finetuning/>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</a></li><li><a href=../../publication/akbc2019-alt-improving/>Improving Relation Extraction by Pre-Trained Language Representations</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/marc-hubner/index.xml b/authors/marc-hubner/index.xml
index 5f9e6215..a386f27c 100644
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diff --git a/authors/maria-kopf/index.xml b/authors/maria-kopf/index.xml
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diff --git a/authors/mariana-neves/index.xml b/authors/mariana-neves/index.xml
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diff --git a/authors/maximilian-dustin-nasert/index.xml b/authors/maximilian-dustin-nasert/index.xml
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diff --git a/authors/neslihan-iskender/index.xml b/authors/neslihan-iskender/index.xml
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diff --git a/authors/niklas-dehio/index.xml b/authors/niklas-dehio/index.xml
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diff --git a/authors/nils-feldhus/index.xml b/authors/nils-feldhus/index.xml
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diff --git a/authors/oliver-sapina/index.xml b/authors/oliver-sapina/index.xml
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diff --git a/authors/oskar-van-der-wal/index.html b/authors/oskar-van-der-wal/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Oskar van der Wal</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/acl2023-sarti-inseq/>Inseq: An Interpretability Toolkit for Sequence Generation Models</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/page/11/index.html b/authors/page/11/index.html
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diff --git a/authors/page/12/index.html b/authors/page/12/index.html
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diff --git a/authors/page/14/index.html b/authors/page/14/index.html
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diff --git a/authors/page/2/index.html b/authors/page/2/index.html
index a1a2b29d..989e0ebc 100644
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diff --git a/authors/page/3/index.html b/authors/page/3/index.html
index 436bf725..580b625a 100644
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diff --git a/authors/page/4/index.html b/authors/page/4/index.html
index 7467dda6..5db73402 100644
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diff --git a/authors/page/5/index.html b/authors/page/5/index.html
index e15ff647..1aa3cbcb 100644
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diff --git a/authors/page/6/index.html b/authors/page/6/index.html
index 58e35611..c227d119 100644
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diff --git a/authors/page/7/index.html b/authors/page/7/index.html
index f9a15f71..88d6d509 100644
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diff --git a/authors/page/8/index.html b/authors/page/8/index.html
index 54961c5f..2f98f07a 100644
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diff --git a/authors/paloma-martinez/index.html b/authors/paloma-martinez/index.html
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diff --git a/authors/peijin-han/index.xml b/authors/peijin-han/index.xml
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diff --git a/authors/peter-bourgonje/index.xml b/authors/peter-bourgonje/index.xml
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Peter Dabrock</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/samhammer-2023-klinische/>Klinische Entscheidungsfindung mit Künstlicher Intelligenz: Ein interdisziplinärer Governance-Ansatz</a></li><li><a href=../../publication/plosone2023-roller-performance/>When performance is not enough—A multidisciplinary view on clinical decision support</a></li><li><a href=../../publication/frontiers2022-roller-nothing/>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</a></li><li><a href=../../publication/frontiers2022-roller-evaluation/>Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/peter-dabrock/index.xml b/authors/peter-dabrock/index.xml
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diff --git a/authors/philippe-thomas/index.html b/authors/philippe-thomas/index.html
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diff --git a/authors/philippe-thomas/index.xml b/authors/philippe-thomas/index.xml
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diff --git a/authors/phuc-tran-truong/index.html b/authors/phuc-tran-truong/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Phuc Tran Truong</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/konvens2021-hennig-mobie/>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/phuc-tran-truong/index.xml b/authors/phuc-tran-truong/index.xml
index 80bbab69..034eb144 100644
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diff --git a/authors/qing-han/index.xml b/authors/qing-han/index.xml
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diff --git a/authors/renlong-ai/index.xml b/authors/renlong-ai/index.xml
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Renzo Rivera-Zavala</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/database2023-roller-biocreative/>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/renzo-rivera-zavala/index.xml b/authors/renzo-rivera-zavala/index.xml
index 9917d7ce..089f365a 100644
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Renzo Rivera-Zavala | DFKI-NLP</title><link>https://dfki-nlp.github.io/authors/renzo-rivera-zavala/</link><atom:link href="https://dfki-nlp.github.io/authors/renzo-rivera-zavala/index.xml" rel="self" type="application/rss+xml"/><description>Renzo Rivera-Zavala</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 15 Feb 2023 10:33:03 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Renzo Rivera-Zavala</title><link>https://dfki-nlp.github.io/authors/renzo-rivera-zavala/</link></image><item><title>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</title><link>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</link><pubDate>Wed, 15 Feb 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</guid><description/></item></channel></rss>
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diff --git a/authors/richard-tzong-han-tsai/index.html b/authors/richard-tzong-han-tsai/index.html
index 0d202ddb..ca348a78 100644
--- a/authors/richard-tzong-han-tsai/index.html
+++ b/authors/richard-tzong-han-tsai/index.html
@@ -3,7 +3,7 @@
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Richard Tzong-Han Tsai</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/database2023-roller-biocreative/>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/richard-tzong-han-tsai/index.xml b/authors/richard-tzong-han-tsai/index.xml
index eb70b04c..f4f7eccb 100644
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diff --git a/authors/robert-schwarzenberg/index.html b/authors/robert-schwarzenberg/index.html
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--- a/authors/robert-schwarzenberg/index.html
+++ b/authors/robert-schwarzenberg/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><section id=profile-page class=pt-5><div class=container><div class=row><div class="col-12 col-lg-4"><div id=profile><img class="avatar avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"><div class=portrait-title><h2>Robert Schwarzenberg</h2><h3>PhD Candidate</h3><h3><a href=https://www.dfki.de target=_blank rel=noopener><span>DFKI</span></a></h3></div><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de aria-label=envelope><i class="fas fa-envelope big-icon"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener aria-label=google-scholar><i class="ai ai-google-scholar big-icon"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener aria-label=github><i class="fab fa-github big-icon"></i></a></li></ul></div></div><div class="col-12 col-lg-8"><div class=article-style><p>I conducted my PhD research at the <a href=https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology/ target=_blank rel=noopener>Speech and Language Technologies Lab</a> of the German Research Center for Artificial Intelligence (DFKI).</p><p>My interests include</p><ul><li>(Neural) Explainability Methods and Explainable Models,</li><li>NLP and NLU,</li><li>some Image Processing on the side,</li><li>and Graph Algorithms because, you see, everything seems to be part of some graph.</li></ul></div><div class=row></div></div></div><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/acl2023-feldhus-smv/>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</a></li><li><a href=../../publication/nlpcc-castle/>Detecting Covariate Drift with Explanations</a></li><li><a href=../../project/deeplee/>DEEPLEE</a></li><li><a href=../../publication/semeval2020-huebner-defx/>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</a></li><li><a href=../../publication/icml-schwarzenberg-castle/>Pattern-Guided Integrated Gradients</a></li><li><a href=../../publication/swisstext2020-zaczynska-evaluating/>Evaluating German Transformer Language Models with Syntactic Agreement Tests</a></li><li><a href=../../publication/lrec2020-aksenov-abstractive/>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</a></li><li><a href=../../publication/cl-nvc/>Cross-lingual Neural Vector Conceptualization</a></li><li><a href=../../publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</a></li><li><a href=../../publication/nvc/>Neural Vector Conceptualization for Word Vector Space Interpretation</a></li><li><a href=../../publication/diamat/>Train, Sort, Explain: Learning to Diagnose Translation Models</a></li><li><a href=../../publication/learning-explanations-from-language-data/>Learning Explanations From Language Data</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/authors/robert-schwarzenberg/index.xml b/authors/robert-schwarzenberg/index.xml
index 95ca5532..e7dca23f 100644
--- a/authors/robert-schwarzenberg/index.xml
+++ b/authors/robert-schwarzenberg/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Robert Schwarzenberg | DFKI-NLP</title><link>https://dfki-nlp.github.io/authors/robert-schwarzenberg/</link><atom:link href="https://dfki-nlp.github.io/authors/robert-schwarzenberg/index.xml" rel="self" type="application/rss+xml"/><description>Robert Schwarzenberg</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Thu, 15 Jun 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png</url><title>Robert Schwarzenberg</title><link>https://dfki-nlp.github.io/authors/robert-schwarzenberg/</link></image><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>Detecting Covariate Drift with Explanations</title><link>https://dfki-nlp.github.io/publication/nlpcc-castle/</link><pubDate>Fri, 15 Oct 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/nlpcc-castle/</guid><description/></item><item><title>DEEPLEE</title><link>https://dfki-nlp.github.io/project/deeplee/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/deeplee/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Pattern-Guided Integrated Gradients</title><link>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</link><pubDate>Wed, 29 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</guid><description/></item><item><title>Evaluating German Transformer Language Models with Syntactic Agreement Tests</title><link>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</link><pubDate>Tue, 23 Jun 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Cross-lingual Neural Vector Conceptualization</title><link>https://dfki-nlp.github.io/publication/cl-nvc/</link><pubDate>Mon, 30 Sep 2019 18:09:58 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/cl-nvc/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Neural Vector Conceptualization for Word Vector Space Interpretation</title><link>https://dfki-nlp.github.io/publication/nvc/</link><pubDate>Tue, 02 Apr 2019 17:55:21 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/nvc/</guid><description/></item><item><title>Train, Sort, Explain: Learning to Diagnose Translation Models</title><link>https://dfki-nlp.github.io/publication/diamat/</link><pubDate>Thu, 28 Mar 2019 17:40:45 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/diamat/</guid><description/></item><item><title>Learning Explanations From Language Data</title><link>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</link><pubDate>Fri, 02 Nov 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Robert Schwarzenberg | DFKI-NLP</title><link>https://dfki-nlp.github.io/authors/robert-schwarzenberg/</link><atom:link href="https://dfki-nlp.github.io/authors/robert-schwarzenberg/index.xml" rel="self" type="application/rss+xml"/><description>Robert Schwarzenberg</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Thu, 15 Jun 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png</url><title>Robert Schwarzenberg</title><link>https://dfki-nlp.github.io/authors/robert-schwarzenberg/</link></image><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>Detecting Covariate Drift with Explanations</title><link>https://dfki-nlp.github.io/publication/nlpcc-castle/</link><pubDate>Fri, 15 Oct 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/nlpcc-castle/</guid><description/></item><item><title>DEEPLEE</title><link>https://dfki-nlp.github.io/project/deeplee/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/deeplee/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Pattern-Guided Integrated Gradients</title><link>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</link><pubDate>Wed, 29 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</guid><description/></item><item><title>Evaluating German Transformer Language Models with Syntactic Agreement Tests</title><link>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</link><pubDate>Tue, 23 Jun 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Cross-lingual Neural Vector Conceptualization</title><link>https://dfki-nlp.github.io/publication/cl-nvc/</link><pubDate>Mon, 30 Sep 2019 18:09:58 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/cl-nvc/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Neural Vector Conceptualization for Word Vector Space Interpretation</title><link>https://dfki-nlp.github.io/publication/nvc/</link><pubDate>Tue, 02 Apr 2019 17:55:21 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/nvc/</guid><description/></item><item><title>Train, Sort, Explain: Learning to Diagnose Translation Models</title><link>https://dfki-nlp.github.io/publication/diamat/</link><pubDate>Thu, 28 Mar 2019 17:40:45 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/diamat/</guid><description/></item><item><title>Learning Explanations From Language Data</title><link>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</link><pubDate>Fri, 02 Nov 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</guid><description/></item></channel></rss>
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diff --git a/authors/roland-roller/index.html b/authors/roland-roller/index.html
index ed283a4b..10cf8e59 100644
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+++ b/authors/roland-roller/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><section id=profile-page class=pt-5><div class=container><div class=row><div class="col-12 col-lg-4"><div id=profile><img class="avatar avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"><div class=portrait-title><h2>Roland Roller</h2><h3>Senior Researcher</h3><h3><a href=https://www.dfki.de target=_blank rel=noopener><span>DFKI</span></a></h3></div><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de aria-label=envelope><i class="fas fa-envelope big-icon"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener aria-label=google-scholar><i class="ai ai-google-scholar big-icon"></i></a></li></ul></div></div><div class="col-12 col-lg-8"><div class=article-style></div><div class=row></div></div></div><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/konvens2023-binsumait-etal-factuality/>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</a></li><li><a href=../../publication/shti2023-abdulnazar-sapbert/>SapBERT-Based Medical Concept Normalization Using SNOMED CT</a></li><li><a href=../../publication/samhammer-2023-klinische/>Klinische Entscheidungsfindung mit Künstlicher Intelligenz: Ein interdisziplinärer Governance-Ansatz</a></li><li><a href=../../publication/plosone2023-roller-performance/>When performance is not enough—A multidisciplinary view on clinical decision support</a></li><li><a href=../../publication/database2023-roller-biocreative/>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</a></li><li><a href=../../publication/frontiers2022-roller-nothing/>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</a></li><li><a href=../../publication/wmt-emnlp2022-roller-findings/>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</a></li><li><a href=../../publication/frontiers2022-roller-evaluation/>Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation</a></li><li><a href=../../publication/lrec2022-roller/>An Annotated Corpus of Textual Explanations for Clinical Decision Support</a></li><li><a href=../../publication/lrec2022-raithel-cross-adr/>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</a></li><li><a href=../../publication/lrec2022-seiffe-subjective/>Subjective Text Complexity Assessment for German</a></li><li><a href=../../publication/mex-an-information-extraction-platform-for-german-medical-text/>mEx - an Information Extraction Platform for German Medical Text</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/roland-roller/index.xml b/authors/roland-roller/index.xml
index c4747d09..43b4ba8e 100644
--- a/authors/roland-roller/index.xml
+++ b/authors/roland-roller/index.xml
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Roland Roller | DFKI-NLP</title><link>https://dfki-nlp.github.io/authors/roland-roller/</link><atom:link href="https://dfki-nlp.github.io/authors/roland-roller/index.xml" rel="self" type="application/rss+xml"/><description>Roland Roller</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Thu, 17 Aug 2023 08:33:03 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg</url><title>Roland Roller</title><link>https://dfki-nlp.github.io/authors/roland-roller/</link></image><item><title>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</title><link>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</link><pubDate>Thu, 17 Aug 2023 08:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</guid><description/></item><item><title>SapBERT-Based Medical Concept Normalization Using SNOMED CT</title><link>https://dfki-nlp.github.io/publication/shti2023-abdulnazar-sapbert/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/shti2023-abdulnazar-sapbert/</guid><description/></item><item><title>Klinische Entscheidungsfindung mit Künstlicher Intelligenz: Ein interdisziplinärer Governance-Ansatz</title><link>https://dfki-nlp.github.io/publication/samhammer-2023-klinische/</link><pubDate>Tue, 02 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/samhammer-2023-klinische/</guid><description/></item><item><title>When performance is not enough—A multidisciplinary view on clinical decision support</title><link>https://dfki-nlp.github.io/publication/plosone2023-roller-performance/</link><pubDate>Sun, 23 Apr 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/plosone2023-roller-performance/</guid><description/></item><item><title>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</title><link>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</link><pubDate>Wed, 15 Feb 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</guid><description/></item><item><title>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</title><link>https://dfki-nlp.github.io/publication/frontiers2022-roller-nothing/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/frontiers2022-roller-nothing/</guid><description/></item><item><title>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</title><link>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</guid><description/></item><item><title>Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation</title><link>https://dfki-nlp.github.io/publication/frontiers2022-roller-evaluation/</link><pubDate>Tue, 25 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/frontiers2022-roller-evaluation/</guid><description/></item><item><title>An Annotated Corpus of Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/publication/lrec2022-roller/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-roller/</guid><description/></item><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item><item><title>Subjective Text Complexity Assessment for German</title><link>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</guid><description/></item><item><title>mEx - an Information Extraction Platform for German Medical Text</title><link>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</link><pubDate>Sat, 01 Dec 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</guid><description/></item></channel></rss>
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diff --git a/authors/sabine-bergler/index.html b/authors/sabine-bergler/index.html
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+++ b/authors/sabine-bergler/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Sabine Bergler</h1></div><section id=profile-page class=pt-5><div class=container><div class="article-widget content-widget-hr"><h3>Latest</h3><ul><li><a href=../../publication/database2023-roller-biocreative/>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</a></li></ul></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/authors/sabine-bergler/index.xml b/authors/sabine-bergler/index.xml
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diff --git a/authors/sebastian-moller/index.xml b/authors/sebastian-moller/index.xml
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diff --git a/authors/simon-gerndt/index.xml b/authors/simon-gerndt/index.xml
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diff --git a/authors/simon-ronicke/index.html b/authors/simon-ronicke/index.html
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diff --git a/authors/tim-polzehl/index.xml b/authors/tim-polzehl/index.xml
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diff --git a/authors/timothy-miller/index.html b/authors/timothy-miller/index.html
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diff --git a/authors/timothy-miller/index.xml b/authors/timothy-miller/index.xml
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diff --git a/authors/ursula-strohriegel/index.xml b/authors/ursula-strohriegel/index.xml
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diff --git a/authors/vincent-vandeghinste/index.xml b/authors/vincent-vandeghinste/index.xml
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diff --git a/authors/vivien-macketanz/index.xml b/authors/vivien-macketanz/index.xml
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diff --git a/categories/index.html b/categories/index.html
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diff --git a/categories/index.xml b/categories/index.xml
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diff --git a/dataset/dfki-mobasa-corpus/index.html b/dataset/dfki-mobasa-corpus/index.html
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diff --git a/dataset/dfki-mobie-corpus/index.html b/dataset/dfki-mobie-corpus/index.html
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diff --git a/dataset/dfki-product-corpus/index.html b/dataset/dfki-product-corpus/index.html
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diff --git a/dataset/dfki-smartdata-corpus/index.html b/dataset/dfki-smartdata-corpus/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class=article><div class="article-container pt-3"><h1>SmartData Corpus</h1><div class=article-metadata><span class=article-date>Dec 16, 2019</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://github.com/DFKI-NLP/smartdata-corpus target=_blank rel=noopener>Go to Project Site</a></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/language-understanding/>Language Understanding</a>
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diff --git a/dataset/ex4cds/index.html b/dataset/ex4cds/index.html
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@@ -5,7 +5,7 @@
 <a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a>
 <a class="badge badge-light" href=../../tag/multilinguality/>Multilinguality</a></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/dataset/index.html b/dataset/index.html
index e162ce99..82886189 100644
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@@ -7,7 +7,7 @@
 Please see [our ACL paper](https://arxiv.org/abs/2305.04582) for full details. You can find the Github repo containing the translation and experiment code here [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED).</div></div><div><h2><a href=https://github.com/DFKI-NLP/Ex4CDS target=_blank rel=noopener>Ex4CDS - Textual Explanations for Clinical Decision Support</a></h2><div class=article-style>Ex4CDS are explanations (or more precisely justifications) of physicians in the context of clinical decision support. In the course of a larger study, physicians estimated the probability of different clinical outcomes in nephology, namely rejection, graft loss and infections, within the next 90 days. Each estimation had to be justified within a short text - these are our explanations. The explanations were provided in German and have strong similarities to general clinical notes. You can find a description and the data here: https://github.com/DFKI-NLP/Ex4CDS</div></div><div><h2><a href=https://github.com/DFKI-NLP/cross-ling-adr target=_blank rel=noopener>German Adverse Drug Reaction (ADR) detection in patient-generated content</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection. More info: https://aclanthology.org/2022.lrec-1.388/</div></div><div><h2><a href=https://github.com/DFKI-NLP/sim3s-corpus target=_blank rel=noopener>MobASA Corpus</a></h2><div class=article-style>This repository contains corpus called MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children.
 The data can be used for as a training or test corpus for aspect-oriented sentiment analysis. Moreover, the corpus can benefit building inclusive public transportation systems. You can find the corpus here: https://github.com/DFKI-NLP/sim3s-corpus, and the description of the corpus here: https://aclanthology.org/2022.csrnlp-1.5.pdf</div></div><div><h2><a href=https://github.com/DFKI-NLP/MobIE target=_blank rel=noopener>MobIE Corpus</a></h2><div class=article-style>This repository contains the DFKI MobIE Corpus (formerly "DAYSTREAM Corpus"), a dataset of 3,232 German-language documents collected between May 2015 - Apr 2019 that have been annotated with fine-grained geo-entities, such as location-street, location-stop and location-route, as well as standard named entity types (organization, date, number, etc). All location-related entities have been linked to either Open Street Map identifiers or database ids of Deutsche Bahn / Rhein-Main-Verkehrsverbund. The corpus has also been annotated with a set of 7 traffic-related n-ary relations and events, such as Accidents, Traffic jams, and Canceled Routes. It consists of Twitter messages, and traffic reports from e.g. radio stations, police and public transport providers. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, entity linking of these entities, as well as n-ary relation extraction systems. You can find the description of the corpus here: https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/11741/</div></div><div><h2><a href=https://github.com/DFKI-NLP/product-corpus target=_blank rel=noopener>Product Corpus</a></h2><div class=article-style>The Product Corpus is a dataset of 174 English web pages and social media posts annotated for product and company named entities, and the relation CompanyProvidesProduct. The goal is to make extraction of non-standard, B2B products and relations from unstructured text easier and more reliable. The corpus is also annotated for coreference chains of companies and products.</div></div><div><h2><a href=https://github.com/DFKI-NLP/smartdata-corpus target=_blank rel=noopener>SmartData Corpus</a></h2><div class=article-style>The SmartData Corpus is a dataset of 2598 German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as Accidents, Traffic jams, Acquisitions, and Strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems.</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
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diff --git a/dataset/index.xml b/dataset/index.xml
index a10b536c..56ffca39 100644
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@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Datasets | DFKI-NLP</title><link>https://dfki-nlp.github.io/dataset/</link><atom:link href="https://dfki-nlp.github.io/dataset/index.xml" rel="self" type="application/rss+xml"/><description>Datasets</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Datasets</title><link>https://dfki-nlp.github.io/dataset/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item><item><title>Ex4CDS - Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/dataset/ex4cds/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/ex4cds/</guid><description/></item><item><title>German Adverse Drug Reaction (ADR) detection in patient-generated content</title><link>https://dfki-nlp.github.io/dataset/german-patient-adr/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/german-patient-adr/</guid><description/></item><item><title>MobASA Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</guid><description/></item><item><title>MobIE Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</guid><description/></item><item><title>Product Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-product-corpus/</link><pubDate>Mon, 16 Dec 2019 15:55:08 +0100</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-product-corpus/</guid><description/></item><item><title>SmartData Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-smartdata-corpus/</link><pubDate>Mon, 16 Dec 2019 15:55:08 +0100</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-smartdata-corpus/</guid><description/></item></channel></rss>
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Datasets | DFKI-NLP</title><link>https://dfki-nlp.github.io/dataset/</link><atom:link href="https://dfki-nlp.github.io/dataset/index.xml" rel="self" type="application/rss+xml"/><description>Datasets</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Datasets</title><link>https://dfki-nlp.github.io/dataset/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item><item><title>Ex4CDS - Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/dataset/ex4cds/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/ex4cds/</guid><description/></item><item><title>German Adverse Drug Reaction (ADR) detection in patient-generated content</title><link>https://dfki-nlp.github.io/dataset/german-patient-adr/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/german-patient-adr/</guid><description/></item><item><title>MobASA Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</guid><description/></item><item><title>MobIE Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</guid><description/></item><item><title>Product Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-product-corpus/</link><pubDate>Mon, 16 Dec 2019 15:55:08 +0100</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-product-corpus/</guid><description/></item><item><title>SmartData Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-smartdata-corpus/</link><pubDate>Mon, 16 Dec 2019 15:55:08 +0100</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-smartdata-corpus/</guid><description/></item></channel></rss>
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diff --git a/dataset/multitacred/index.html b/dataset/multitacred/index.html
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 <a class="badge badge-light" href=../../tag/multilinguality/>Multilinguality</a>
 <a class="badge badge-light" href=../../tag/transfer-learning/>Transfer Learning</a></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/event/index.html b/event/index.html
index 9eb48c3a..c17d2a84 100644
--- a/event/index.html
+++ b/event/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Recent & Upcoming Events</h1></div><div class=universal-wrapper></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/event/index.xml b/event/index.xml
index 9403630d..50c83182 100644
--- a/event/index.xml
+++ b/event/index.xml
@@ -1 +1 @@
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Recent &amp; Upcoming Events | DFKI-NLP</title><link>https://dfki-nlp.github.io/event/</link><atom:link href="https://dfki-nlp.github.io/event/index.xml" rel="self" type="application/rss+xml"/><description>Recent &amp; Upcoming Events</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Recent &amp; Upcoming Events</title><link>https://dfki-nlp.github.io/event/</link></image></channel></rss>
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diff --git a/index.html b/index.html
index 2163d010..0de0013d 100644
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@@ -9,7 +9,7 @@
 <span class=middot-divider></span><span class=article-reading-time>2 min read</span></div><div class="section-subheading article-title mb-1 mt-3"><a href=./post/acl2023/>5 papers to be presented at ACL 2023</a></div><a href=./post/acl2023/ class=summary-link><div class=article-style><p>Five papers from DFKI-NLP researchers have been accepted for publication at ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics. The conference is planned to be a hybrid meeting and will take place in Toronto, Canada, from Jul 9th through July 14th, 2023.</p></div></a></div><div class=card-simple><div class=article-metadata><span class=article-date>Oct 14, 2022</span>
 <span class=middot-divider></span><span class=article-reading-time>1 min read</span></div><div class="section-subheading article-title mb-1 mt-3"><a href=./post/emnlp2022/>2 papers to be presented at EMNLP 2022</a></div><a href=./post/emnlp2022/ class=summary-link><div class=article-style><p>Two papers from DFKI-NLP authors have been accepted for publication at EMNLP 2022, the 2022 Conference on Empirical Methods in Natural Language Processing. The conference is planned to be a hybrid meeting and will take place in Abu Dhabi, from Dec 7th to Dec 11th, 2022.</p></div></a></div><div class=card-simple><div class=article-metadata><span class=article-date>Oct 14, 2022</span>
 <span class=middot-divider></span><span class=article-reading-time>1 min read</span></div><div class="section-subheading article-title mb-1 mt-3"><a href=./post/aacl-ijcnlp2022/>1 paper to be presented at AACL-IJCNLP 2022</a></div><a href=./post/aacl-ijcnlp2022/ class=summary-link><div class=article-style><p>One paper from DFKI-NLP authors has been accepted for publication at the Workshop on Information Extraction from Scientific Publications (WIESP). The workshop will be held at AACL-IJCNLP 2022, the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, which will take place as an online-only event from Nov.</p></div></a></div><div class=see-all><a href=./post/>See all posts
-<i class="fas fa-angle-right"></i></a></div></div></div></div></section><section id=people class="home-section wg-people"><div class=home-section-bg></div><div class=container><div class="row justify-content-center people-widget"><div class="col-md-12 section-heading"><h1>People</h1></div><div class=col-md-12><h2 class=mb-4>Researchers</h2></div><div class="col-12 col-sm-auto people-person"><a href=./authors/leonhard-hennig/><img class="avatar avatar-circle" src=./authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=./authors/leonhard-hennig/>Leonhard Hennig</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/roland-roller/><img class="avatar avatar-circle" src=./authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/roland-roller/>Roland Roller</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/eleftherios-avramidis/><img class="avatar avatar-circle" src=./authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/philippe-thomas/><img class="avatar avatar-circle" src=./authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/philippe-thomas/>Philippe Thomas</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class=col-md-12><h2 class=mb-4>PhD Candidates</h2></div><div class="col-12 col-sm-auto people-person"><a href=./authors/nils-feldhus/><img class="avatar avatar-circle" src=./authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/nils-feldhus/>Nils Feldhus</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/aleksandra-gabryszak/><img class="avatar avatar-circle" src=./authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/malte-ostendorff/><img class="avatar avatar-circle" src=./authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/malte-ostendorff/>Malte Ostendorff</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/nils-rethmeier/><img class="avatar avatar-circle" src=./authors/nils-rethmeier/avatar_hu06230aa9f5e4bab2d279f74300374684_30872_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/nils-rethmeier/>Nils Rethmeier</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.rethmeier@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/NilsRethmeier target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/steffen-castle/><img class="avatar avatar-circle" src=./authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/steffen-castle/>Steffen Castle</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/david-harbecke/><img class="avatar avatar-circle" src=./authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/david-harbecke/>David Harbecke</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/yuxuan-chen/><img class="avatar avatar-circle" src=./authors/yuxuan-chen/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/yuxuan-chen/>Yuxuan Chen</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:yuxuan.chen@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/chen-yuxuan target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/arne-binder/><img class="avatar avatar-circle" src=./authors/arne-binder/avatar_huf1b341a11428bbdba6a06391dba9a6b4_16118_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/arne-binder/>Arne Binder</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:arne.binder@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/ArneBinder target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/lisa-raithel/><img class="avatar avatar-circle" src=./authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/lisa-raithel/>Lisa Raithel</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.linkedin.com/in/lisa-raithel-3b2865240/ target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/ajaymadhavan-ravichandran/><img class="avatar avatar-circle" src=./authors/ajaymadhavan-ravichandran/avatar_hua10ca1baf889e84554810956702664b3_23223_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/ajaymadhavan-ravichandran/>Ajay Madhavan Ravichandran</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:ajay_madhavan.ravichandran@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/aj280192 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class=col-md-12><h2 class=mb-4>Alumni</h2></div><div class="col-12 col-sm-auto people-person"><a href=./authors/robert-schwarzenberg/><img class="avatar avatar-circle" src=./authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=./authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/he-wang/><img class="avatar avatar-circle" src=./authors/he-wang/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/he-wang/>He Wang</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:he.wang@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/wanghe001 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/christoph-alt/><img class="avatar avatar-circle" src=./authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/christoph-alt/>Christoph Alt</a></h2><ul class=network-icon aria-hidden=true><li><a href=./#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/karolina-zaczynska/><img class="avatar avatar-circle" src=./authors/karolina-zaczynska/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/karolina-zaczynska/>Karolina Zaczynska</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:karolina.zaczynska@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/marc-huebner/><img class="avatar avatar-circle" src=./authors/marc-huebner/avatar_hue3039d0434e4d36a6cc838ce7e754cdd_20432_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/marc-huebner/>Marc Hübner</a></h2><ul class=network-icon aria-hidden=true><li><a href=https://github.com/marchbnr target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></section><section id=publications class="home-section wg-pages"><div class=home-section-bg></div><div class=container><div class=row><div class="section-heading col-12 col-lg-4 mb-3 mb-lg-0 d-flex flex-column align-items-center align-items-lg-start"><h1 class=mb-0>Recent Publications</h1></div><div class="col-12 col-lg-8"><div class=card-simple><div class=article-metadata><div><span><a href=./authors/nils-feldhus/>Nils Feldhus</a></span>, <span><a href=./authors/qianli-wang/>Qianli Wang</a></span>, <span><a href=./authors/tatiana-anikina/>Tatiana Anikina</a></span>, <span><a href=./authors/sahil-chopra/>Sahil Chopra</a></span>, <span><a href=./authors/cennet-oguz/>Cennet Oguz</a></span>, <span><a href=./authors/sebastian-moller/>Sebastian Möller</a></span></div><span class=article-date>December 2023</span>
+<i class="fas fa-angle-right"></i></a></div></div></div></div></section><section id=people class="home-section wg-people"><div class=home-section-bg></div><div class=container><div class="row justify-content-center people-widget"><div class="col-md-12 section-heading"><h1>People</h1></div><div class=col-md-12><h2 class=mb-4>Researchers</h2></div><div class="col-12 col-sm-auto people-person"><a href=./authors/leonhard-hennig/><img class="avatar avatar-circle" src=./authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=./authors/leonhard-hennig/>Leonhard Hennig</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/roland-roller/><img class="avatar avatar-circle" src=./authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/roland-roller/>Roland Roller</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/eleftherios-avramidis/><img class="avatar avatar-circle" src=./authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/philippe-thomas/><img class="avatar avatar-circle" src=./authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/philippe-thomas/>Philippe Thomas</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class=col-md-12><h2 class=mb-4>PhD Candidates</h2></div><div class="col-12 col-sm-auto people-person"><a href=./authors/nils-feldhus/><img class="avatar avatar-circle" src=./authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/nils-feldhus/>Nils Feldhus</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/aleksandra-gabryszak/><img class="avatar avatar-circle" src=./authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/malte-ostendorff/><img class="avatar avatar-circle" src=./authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/malte-ostendorff/>Malte Ostendorff</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/steffen-castle/><img class="avatar avatar-circle" src=./authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/steffen-castle/>Steffen Castle</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/david-harbecke/><img class="avatar avatar-circle" src=./authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/david-harbecke/>David Harbecke</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank 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class=portrait-title><h2><a href=./authors/arne-binder/>Arne Binder</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:arne.binder@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/ArneBinder target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/lisa-raithel/><img class="avatar avatar-circle" src=./authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/lisa-raithel/>Lisa Raithel</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener><i class="fab 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src=./authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=./authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/nils-rethmeier/><img class="avatar avatar-circle" src=./authors/nils-rethmeier/avatar_hu06230aa9f5e4bab2d279f74300374684_30872_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/nils-rethmeier/>Nils Rethmeier</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.rethmeier@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/NilsRethmeier target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/he-wang/><img class="avatar avatar-circle" src=./authors/he-wang/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/he-wang/>He Wang</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:he.wang@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/wanghe001 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/christoph-alt/><img class="avatar avatar-circle" src=./authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/christoph-alt/>Christoph Alt</a></h2><ul class=network-icon aria-hidden=true><li><a href=./#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/karolina-zaczynska/><img class="avatar avatar-circle" src=./authors/karolina-zaczynska/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/karolina-zaczynska/>Karolina Zaczynska</a></h2><ul class=network-icon aria-hidden=true><li><a href=mailto:karolina.zaczynska@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=./authors/marc-huebner/><img class="avatar avatar-circle" src=./authors/marc-huebner/avatar_hue3039d0434e4d36a6cc838ce7e754cdd_20432_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=./authors/marc-huebner/>Marc Hübner</a></h2><ul class=network-icon aria-hidden=true><li><a href=https://github.com/marchbnr target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></section><section id=publications class="home-section wg-pages"><div class=home-section-bg></div><div class=container><div class=row><div class="section-heading col-12 col-lg-4 mb-3 mb-lg-0 d-flex flex-column align-items-center align-items-lg-start"><h1 class=mb-0>Recent Publications</h1></div><div class="col-12 col-lg-8"><div class=card-simple><div class=article-metadata><div><span><a href=./authors/nils-feldhus/>Nils Feldhus</a></span>, <span><a href=./authors/qianli-wang/>Qianli Wang</a></span>, <span><a href=./authors/tatiana-anikina/>Tatiana Anikina</a></span>, <span><a href=./authors/sahil-chopra/>Sahil Chopra</a></span>, <span><a href=./authors/cennet-oguz/>Cennet Oguz</a></span>, <span><a href=./authors/sebastian-moller/>Sebastian Möller</a></span></div><span class=article-date>December 2023</span>
 <span class=middot-divider></span><span class=pub-publication>Findings EMNLP 2023</span></div><div class="section-subheading article-title mb-1 mt-3"><a href=./publication/emnlp2023-feldhus-interrolang/>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</a></div><a href=./publication/emnlp2023-feldhus-interrolang/ class=summary-link><div class=article-style><p>While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such a dialogue system can help users explore datasets and models with explanations in a contextualized manner, e.g. via clarification or follow-up questions, and through a natural language interface. We adapt the conversational explanation framework TalkToModel (Slack et al., 2022) to the NLP domain, add new NLP-specific operations such as free-text rationalization, and illustrate its generalizability on three NLP tasks (dialogue act classification, question answering, hate speech detection). To recognize user queries for explanations, we evaluate fine-tuned and few-shot prompting models and implement a novel adapter-based approach. We then conduct two user studies on (1) the perceived correctness and helpfulness of the dialogues, and (2) the simulatability, i.e. how objectively helpful dialogical explanations are for humans in figuring out the model&rsquo;s predicted label when it&rsquo;s not shown. We found rationalization and feature attribution were helpful in explaining the model behavior. Moreover, users could more reliably predict the model outcome based on an explanation dialogue rather than one-off explanations.</p></div></a><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://arxiv.org/pdf/2310.05592.pdf target=_blank rel=noopener>PDF</a>
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 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/DFKI-NLP/InterroLang target=_blank rel=noopener>Code</a>
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diff --git a/index.xml b/index.xml
index 962bcc05..c1e54637 100644
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DFKI-NLP</title><link>https://dfki-nlp.github.io/</link><atom:link href="https://dfki-nlp.github.io/index.xml" rel="self" type="application/rss+xml"/><description>DFKI-NLP</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 06 Dec 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>DFKI-NLP</title><link>https://dfki-nlp.github.io/</link></image><item><title>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</title><link>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</link><pubDate>Wed, 06 Dec 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</guid><description/></item><item><title>1 paper accepted to EMNLP Findings 2023</title><link>https://dfki-nlp.github.io/post/emnlp2023/</link><pubDate>Sat, 14 Oct 2023 09:24:01 +0200</pubDate><guid>https://dfki-nlp.github.io/post/emnlp2023/</guid><description>&lt;p>One paper from DFKI-NLP authors has been accepted for publication as a Findings paper at &lt;a href="https://2023.emnlp.org/" target="_blank" rel="noopener">EMNLP 2023&lt;/a>, the 2023 Conference on Empirical Methods in Natural Language Processing. The conference is planned to be a hybrid meeting and will take place in Singapore, from Dec 6th to Dec 10th, 2023. The paper introduces the InterroLang framework for dialog-based explanations of NLP models and datasets. It adapts the conversational explanation framework TalkToModel to the NLP domain, and add new NLP-specific operations such as free-text rationalizations.&lt;/p>
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index 2a4ecb4a..00e43513 100644
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-<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><span class="js-widget-page d-none"></span><section id=people class="home-section wg-people"><div class=home-section-bg></div><div class=container><div class="row justify-content-center people-widget"><div class="col-md-12 section-heading"><h1>People</h1></div><div class=col-md-12><h2 class=mb-4>Researchers</h2></div><div class="col-12 col-sm-auto people-person"><a href=../authors/leonhard-hennig/><img class="avatar avatar-circle" src=../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=../authors/leonhard-hennig/>Leonhard Hennig</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/roland-roller/><img class="avatar avatar-circle" src=../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/roland-roller/>Roland Roller</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/eleftherios-avramidis/><img class="avatar avatar-circle" src=../authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/philippe-thomas/><img class="avatar avatar-circle" src=../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/philippe-thomas/>Philippe Thomas</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class=col-md-12><h2 class=mb-4>PhD Candidates</h2></div><div class="col-12 col-sm-auto people-person"><a href=../authors/nils-feldhus/><img class="avatar avatar-circle" src=../authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/nils-feldhus/>Nils Feldhus</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/aleksandra-gabryszak/><img class="avatar avatar-circle" src=../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/malte-ostendorff/><img class="avatar avatar-circle" src=../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/malte-ostendorff/>Malte Ostendorff</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/nils-rethmeier/><img class="avatar avatar-circle" src=../authors/nils-rethmeier/avatar_hu06230aa9f5e4bab2d279f74300374684_30872_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/nils-rethmeier/>Nils Rethmeier</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.rethmeier@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/NilsRethmeier target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/steffen-castle/><img class="avatar avatar-circle" src=../authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/steffen-castle/>Steffen Castle</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/david-harbecke/><img class="avatar avatar-circle" src=../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/david-harbecke/>David Harbecke</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/yuxuan-chen/><img class="avatar avatar-circle" src=../authors/yuxuan-chen/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/yuxuan-chen/>Yuxuan Chen</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:yuxuan.chen@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/chen-yuxuan target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/arne-binder/><img class="avatar avatar-circle" src=../authors/arne-binder/avatar_huf1b341a11428bbdba6a06391dba9a6b4_16118_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/arne-binder/>Arne Binder</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:arne.binder@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/ArneBinder target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/lisa-raithel/><img class="avatar avatar-circle" src=../authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/lisa-raithel/>Lisa Raithel</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.linkedin.com/in/lisa-raithel-3b2865240/ target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/ajaymadhavan-ravichandran/><img class="avatar avatar-circle" src=../authors/ajaymadhavan-ravichandran/avatar_hua10ca1baf889e84554810956702664b3_23223_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/ajaymadhavan-ravichandran/>Ajay Madhavan Ravichandran</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:ajay_madhavan.ravichandran@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/aj280192 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class=col-md-12><h2 class=mb-4>Alumni</h2></div><div class="col-12 col-sm-auto people-person"><a href=../authors/robert-schwarzenberg/><img class="avatar avatar-circle" src=../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/he-wang/><img class="avatar avatar-circle" src=../authors/he-wang/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/he-wang/>He Wang</a></h2><h3>Software Engineer</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:he.wang@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/wanghe001 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/christoph-alt/><img class="avatar avatar-circle" src=../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/christoph-alt/>Christoph Alt</a></h2><ul class=network-icon aria-hidden=true><li><a href=../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/karolina-zaczynska/><img class="avatar avatar-circle" src=../authors/karolina-zaczynska/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/karolina-zaczynska/>Karolina Zaczynska</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:karolina.zaczynska@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/marc-huebner/><img class="avatar avatar-circle" src=../authors/marc-huebner/avatar_hue3039d0434e4d36a6cc838ce7e754cdd_20432_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/marc-huebner/>Marc Hübner</a></h2><h3>Alumni</h3><ul class=network-icon aria-hidden=true><li><a href=https://github.com/marchbnr target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
+<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><span class="js-widget-page d-none"></span><section id=people class="home-section wg-people"><div class=home-section-bg></div><div class=container><div class="row justify-content-center people-widget"><div class="col-md-12 section-heading"><h1>People</h1></div><div class=col-md-12><h2 class=mb-4>Researchers</h2></div><div class="col-12 col-sm-auto people-person"><a href=../authors/leonhard-hennig/><img class="avatar avatar-circle" src=../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=../authors/leonhard-hennig/>Leonhard Hennig</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/roland-roller/><img class="avatar avatar-circle" src=../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/roland-roller/>Roland Roller</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/eleftherios-avramidis/><img class="avatar avatar-circle" src=../authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/philippe-thomas/><img class="avatar avatar-circle" src=../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/philippe-thomas/>Philippe Thomas</a></h2><h3>Senior Researcher</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class=col-md-12><h2 class=mb-4>PhD Candidates</h2></div><div class="col-12 col-sm-auto people-person"><a href=../authors/nils-feldhus/><img class="avatar avatar-circle" src=../authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/nils-feldhus/>Nils Feldhus</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/aleksandra-gabryszak/><img class="avatar avatar-circle" src=../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/malte-ostendorff/><img class="avatar avatar-circle" src=../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/malte-ostendorff/>Malte Ostendorff</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/steffen-castle/><img class="avatar avatar-circle" src=../authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/steffen-castle/>Steffen Castle</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/david-harbecke/><img class="avatar avatar-circle" src=../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/david-harbecke/>David Harbecke</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/yuxuan-chen/><img class="avatar avatar-circle" src=../authors/yuxuan-chen/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/yuxuan-chen/>Yuxuan Chen</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:yuxuan.chen@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/chen-yuxuan target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/arne-binder/><img class="avatar avatar-circle" src=../authors/arne-binder/avatar_huf1b341a11428bbdba6a06391dba9a6b4_16118_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/arne-binder/>Arne Binder</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:arne.binder@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/ArneBinder target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/lisa-raithel/><img class="avatar avatar-circle" src=../authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/lisa-raithel/>Lisa Raithel</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.linkedin.com/in/lisa-raithel-3b2865240/ target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/ajaymadhavan-ravichandran/><img class="avatar avatar-circle" src=../authors/ajaymadhavan-ravichandran/avatar_hua10ca1baf889e84554810956702664b3_23223_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/ajaymadhavan-ravichandran/>Ajay Madhavan Ravichandran</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:ajay_madhavan.ravichandran@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/aj280192 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class=col-md-12><h2 class=mb-4>Alumni</h2></div><div class="col-12 col-sm-auto people-person"><a href=../authors/robert-schwarzenberg/><img class="avatar avatar-circle" src=../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt=Avatar></a><div class=portrait-title><h2><a href=../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/nils-rethmeier/><img class="avatar avatar-circle" src=../authors/nils-rethmeier/avatar_hu06230aa9f5e4bab2d279f74300374684_30872_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/nils-rethmeier/>Nils Rethmeier</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.rethmeier@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/NilsRethmeier target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/he-wang/><img class="avatar avatar-circle" src=../authors/he-wang/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/he-wang/>He Wang</a></h2><h3>Software Engineer</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:he.wang@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/wanghe001 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/christoph-alt/><img class="avatar avatar-circle" src=../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/christoph-alt/>Christoph Alt</a></h2><ul class=network-icon aria-hidden=true><li><a href=../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/karolina-zaczynska/><img class="avatar avatar-circle" src=../authors/karolina-zaczynska/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/karolina-zaczynska/>Karolina Zaczynska</a></h2><h3>PhD Candidate</h3><ul class=network-icon aria-hidden=true><li><a href=mailto:karolina.zaczynska@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="col-12 col-sm-auto people-person"><a href=../authors/marc-huebner/><img class="avatar avatar-circle" src=../authors/marc-huebner/avatar_hue3039d0434e4d36a6cc838ce7e754cdd_20432_270x270_fill_q90_lanczos_center.jpg alt=Avatar></a><div class=portrait-title><h2><a href=../authors/marc-huebner/>Marc Hübner</a></h2><h3>Alumni</h3><ul class=network-icon aria-hidden=true><li><a href=https://github.com/marchbnr target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></section></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
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diff --git a/post/aacl-ijcnlp2022/index.html b/post/aacl-ijcnlp2022/index.html
index 6a247632..6469f974 100644
--- a/post/aacl-ijcnlp2022/index.html
+++ b/post/aacl-ijcnlp2022/index.html
@@ -10,7 +10,7 @@
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/DFKI-NLP/sam target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/cora4nlp/>Project</a></p></div></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/post/acl2022/index.html b/post/acl2022/index.html
index 0956370f..ceaa208e 100644
--- a/post/acl2022/index.html
+++ b/post/acl2022/index.html
@@ -26,7 +26,7 @@
 HumEval @ ACL 2022.<p><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://aclanthology.org/2022.humeval-1.3.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/acl2022-humeval-macketanz-perceptual/cite.bib>Cite</a></p></div></p></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/post/acl2023/index.html b/post/acl2023/index.html
index 94617474..97ec35fa 100644
--- a/post/acl2023/index.html
+++ b/post/acl2023/index.html
@@ -39,7 +39,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/acl2023-feldhus-smv/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/DFKI-NLP/SMV/ target=_blank rel=noopener>Code</a></p></div></p></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/post/emnlp2022/index.html b/post/emnlp2022/index.html
index a4eda7b2..6ddc5388 100644
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+++ b/post/emnlp2022/index.html
@@ -17,7 +17,7 @@
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/malteos/scincl target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.48550/arXiv.2202.06671 target=_blank rel=noopener>DOI</a></p></div></p></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/post/emnlp2023/index.html b/post/emnlp2023/index.html
index 89a35af9..136a79c0 100644
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@@ -10,7 +10,7 @@
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/DFKI-NLP/InterroLang target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.48550/arXiv.2310.05592 target=_blank rel=noopener>DOI</a></p></div></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/post/index.html b/post/index.html
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--- a/post/index.html
+++ b/post/index.html
@@ -13,7 +13,7 @@
 <span class=middot-divider></span><span class=article-reading-time>2 min read</span></div></div></div><div class=ml-3></div></div><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../post/acl2022/>4 papers to be presented at ACL 2022</a></div><a href=../post/acl2022/ class=summary-link><div class=article-style>Four papers from DFKI-NLP authors have been accepted for publication at ACL 2022, the 60th Annual Meeting of the Association for Computational Linguistics. The conference is planned to be a hybrid meeting and will take place in Dublin, Ireland, from May 22nd through May 27th, 2022.</div></a><div class="stream-meta article-metadata"><div class=article-metadata><span class=article-date>Mar 31, 2022</span>
 <span class=middot-divider></span><span class=article-reading-time>2 min read</span></div></div></div><div class=ml-3></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
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diff --git a/post/index.xml b/post/index.xml
index eca39960..5b58f21c 100644
--- a/post/index.xml
+++ b/post/index.xml
@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Latest News | DFKI-NLP</title><link>https://dfki-nlp.github.io/post/</link><atom:link href="https://dfki-nlp.github.io/post/index.xml" rel="self" type="application/rss+xml"/><description>Latest News</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Sat, 14 Oct 2023 09:24:01 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Latest News</title><link>https://dfki-nlp.github.io/post/</link></image><item><title>1 paper accepted to EMNLP Findings 2023</title><link>https://dfki-nlp.github.io/post/emnlp2023/</link><pubDate>Sat, 14 Oct 2023 09:24:01 +0200</pubDate><guid>https://dfki-nlp.github.io/post/emnlp2023/</guid><description>&lt;p>One paper from DFKI-NLP authors has been accepted for publication as a Findings paper at &lt;a href="https://2023.emnlp.org/" target="_blank" rel="noopener">EMNLP 2023&lt;/a>, the 2023 Conference on Empirical Methods in Natural Language Processing. The conference is planned to be a hybrid meeting and will take place in Singapore, from Dec 6th to Dec 10th, 2023. The paper introduces the InterroLang framework for dialog-based explanations of NLP models and datasets. It adapts the conversational explanation framework TalkToModel to the NLP domain, and add new NLP-specific operations such as free-text rationalizations.&lt;/p>
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Latest News | DFKI-NLP</title><link>https://dfki-nlp.github.io/post/</link><atom:link href="https://dfki-nlp.github.io/post/index.xml" rel="self" type="application/rss+xml"/><description>Latest News</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Sat, 14 Oct 2023 09:24:01 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Latest News</title><link>https://dfki-nlp.github.io/post/</link></image><item><title>1 paper accepted to EMNLP Findings 2023</title><link>https://dfki-nlp.github.io/post/emnlp2023/</link><pubDate>Sat, 14 Oct 2023 09:24:01 +0200</pubDate><guid>https://dfki-nlp.github.io/post/emnlp2023/</guid><description>&lt;p>One paper from DFKI-NLP authors has been accepted for publication as a Findings paper at &lt;a href="https://2023.emnlp.org/" target="_blank" rel="noopener">EMNLP 2023&lt;/a>, the 2023 Conference on Empirical Methods in Natural Language Processing. The conference is planned to be a hybrid meeting and will take place in Singapore, from Dec 6th to Dec 10th, 2023. The paper introduces the InterroLang framework for dialog-based explanations of NLP models and datasets. It adapts the conversational explanation framework TalkToModel to the NLP domain, and add new NLP-specific operations such as free-text rationalizations.&lt;/p>
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diff --git a/post/jcdl2022/index.html b/post/jcdl2022/index.html
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 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/malteos/aspect-document-embeddings target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.1145/3529372.3530912 target=_blank rel=noopener>DOI</a></p></div></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/post/konvens2023/index.html b/post/konvens2023/index.html
index 706c4dc8..2696dff2 100644
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@@ -9,7 +9,7 @@
 KONVENS 2023.<p><a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/konvens2023-binsumait-etal-factuality/cite.bib>Cite</a>
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diff --git a/post/lrec2022/index.html b/post/lrec2022/index.html
index 0161fb19..8a04d8f5 100644
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@@ -30,7 +30,7 @@
 LREC 2022.<p><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://aclanthology.org/2022.lrec-1.175.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/lrec2022-calizzano/cite.bib>Cite</a></p></div></p></div><div class="media author-card content-widget-hr"><a href=../../authors/dfki-nlp/><img class="avatar mr-3 avatar-circle" src=../../authors/dfki-nlp/avatar_hu86991b4a6282a269fd129e35cbb26bc7_452250_270x270_fill_q90_lanczos_center.jpg alt=DFKI-NLP></a><div class=media-body><h5 class=card-title><a href=../../authors/dfki-nlp/>DFKI-NLP</a></h5><h6 class=card-subtitle>Research Group at DFKI</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:git.nlp@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/dfki-nlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://huggingface.co/DFKI-SLT target=_blank rel=noopener><i class="fas fa-grin"></i></a></li></ul></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/privacy/index.html b/privacy/index.html
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In this data protection policy we inform you about the processing of your personal data when visiting and using our web site.</p><h2 id=responsible-service-provider>Responsible service provider</h2><p>German Research Center for Artificial Intelligence (DFKI)<br>Phone: +49 (0)631 / 205 75-0, Email: <a href=mailto:info@dfki.de>info@dfki.de</a></p><h2 id=data-protection-officer>Data Protection Officer</h2><p>Phone: +49 (0)631 / 205 75-0<br>Email: <a href=mailto:datenschutz@dfki.de>datenschutz@dfki.de</a></p><h2 id=intended-use>Intended use</h2><ul><li>Provision of the information offering in the course of the public communication of the DFKI</li><li>Establishment of contact and correspondence with visitors and users</li></ul><h2 id=anonymous-and-protected-use>Anonymous and protected use</h2><p>Visit and usage of our web site are anonymous. At our web site personal data are only collected to the technically necessary extent. The processed data will not be transmitted to any third parties or otherwise disclosed, except on the basis of concrete lawful obligations. Within our information offering we do not embed information or service offerings of third party providers.</p><p>While using our web site the data transmission in the internet is being protected by a generally accepted secure encryption procedure and hence cannot easily be eavesdropped or tampered.</p><h2 id=access-data>Access data</h2><p>Every time our website is accessed, usage, traffic and connection data are collected, temporarily stored in a log file and generally deleted after 90 days.</p><p>The following data is stored for each access/retrieval:</p><ul><li>IP address</li><li>Transmitted user agent information (in particular type/version of web browser, operating system, etc.)</li><li>Transmitted referrer information (URL of the referring page)</li><li>Date and time of access/retrieval</li><li>Submitted Access Method/Function</li><li>Transmitted input values ​​(search terms, etc.)</li><li>Accessed page or retrieved file</li><li>Amount of data transferred</li><li>Access/retrieval processing status</li></ul><p>The processing of the access data is lawful because it is necessary for the purposes of the legitimate interests pursued by DFKI. The legitimate interests pursued by DFKI are the adaptation and optimisation of the information offering and the investigation, detection and prosecution of illegal activities in connection with the usage of our web site.</p><p>The stored data records can be statistically evaluated in order to adapt and optimize our web site to the needs of our visitors. Any techniques that offer the possibility to retrace the access characteristics of users (tracking) will not be applied. The creation of user profiles and automated decision-making based on it is precluded.</p><p>The stored data records are not attributable to specific persons. They are generally not being combined with other data sources. However, the stored data can be analysed and combined with other data sources, if we become aware of concrete indications of any illegal usage.</p><h2 id=cookies>Cookies</h2><p>We use so-called cookies on our web site. Cookies are small files that are being stored by your web browser. The cookies used on our web site do not harm your computer and do not contain any malicious software. They offer a user-friendly and effective usage of our web site. We do not use cookies for marketing purposes.</p><p>We transmit so-called session cookies to your web browser. They are valid only for the duration of your visit on our web site and they do not have any meaning outside of our web site. The session cookies are needed in order to identify your session with a unique number during your visit and to transmit our contents in your preferred language. At the end of your visit the session cookies will be automatically deleted upon termination of your web browser.</p><p>We also transmit permanent cookies to your web browser with a validity period of at most 365 days. We are exclusively using these cookies in order to respect your settings for the type of presentation (normal, inverted) and for the font size. Furthermore, it will be recorded whether you’ve taken notice of the information about the usage of cookies in your web browser.</p><p>You can adjust your web browser such that you will be informed on setting cookies and allow cookies on an individual basis resp. exclude the acceptance of cookies for specific cases or generally. You also can adjust the automatic deletion of cookies upon termination of your web browser. Upon deactivation of cookies the functionality of our web site can be limited. In any case, our information offering is available to its full extent.</p><h2 id=social-media>Social media</h2><p>We do not embed social media plug-ins on our web site. When you are visiting our web site no data are transmitted to social media services. Profiling by any third parties hence is precluded. You though have the option to change over to our information offerings on Facebook, Twitter and YouTube. For the usage of these services we refer to the data protection policies of the respective service providers. We are processing your personal data within the social networks insofar as you post contributions, send messages or otherwise communicate with us.</p><h2 id=correspondence>Correspondence</h2><p>You have the option to contact us by e-mail. We will use your e-mail address and other personal contact data for the correspondence with you. Due to lawful obligation every e-mail correspondence will be archived. Subject to our legitimate interests your e-mail address and other personal contact data can be stored in our contact data base. In this case you will receive a corresponding information on the processing of your contact data.</p><h2 id=access-and-intervention>Access and Intervention</h2><p>Besides the information in this data protection policy you have the right of access to your personal data. To ensure fair data processing, you have the following rights:</p><ul><li>The right to rectification and completion of your personal data</li><li>The right to erasure of your personal data</li><li>The right to restriction of the processing of your personal data</li><li>The right to object to the processing of your personal data on grounds related to your particular situation</li></ul><p>To exercise these rights, please contact our data protection officer.</p><h2 id=right-to-lodge-a-complaint>Right to lodge a complaint</h2><p>You have the right to lodge a complaint with a supervisory authority if you consider that the processing of your personal data infringes statutory data protection regulations.</p></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
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 <!doctype html><html lang=en-us><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=generator content="Wowchemy 5.0.0-beta.2 for Hugo"><meta name=author content="DFKI-NLP"><meta name=description content="In order to optimally prepare industry, science and the society in Germany and Europe for the global Big Data trend, highly coordinated activities in research, teaching, and technology transfer regarding the integration of data analysis methods and scalable data processing are required. 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To achieve this, the Berlin Big Data Center is pursuing the following seven objectives: 1) Pooling expertise in scalable data management, data analytics, and big data application 2) Conducting fundamental research to develop novel and automatically scalable technologies capable of performing 'Deep Analysis' of 'Big Data'. 3) Developing an integrated, declarative, highly scalable open-source system that enables the specification, automatic optimization, parallelization and hardware adaptation, and fault-tolerant, efficient execution of advanced data analysis problems, using varying methods (e.g., drawn from machine learning, linear algebra, statistics and probability theory, computational linguistics, or signal processing), leveraging our work on Apache Flink 4) Transfering technology and know-how to support innovation in companies and startups. 5) Educating data scientists with respect to the five big data dimensions (i.e., applications, economic, legal, social, and technological) via leading educational programs. 6) Empowering people to leverage 'Smart Data', i.e., to discover newfound information based on their massive data sets. 7)Enabling the general public to conduct sound data-driven decision-making."><meta property="og:image" content="https://dfki-nlp.github.io/project/bbdc2/featured.png"><meta property="twitter:image" content="https://dfki-nlp.github.io/project/bbdc2/featured.png"><meta property="og:locale" content="en-us"><meta property="article:published_time" content="2021-02-23T11:16:31+01:00"><meta property="article:modified_time" content="2021-02-23T11:16:31+01:00"><script type=application/ld+json>{"@context":"https://schema.org","@type":"Article","mainEntityOfPage":{"@type":"WebPage","@id":"https://dfki-nlp.github.io/project/bbdc2/"},"headline":"BBDC2","image":["https://dfki-nlp.github.io/project/bbdc2/featured.png"],"datePublished":"2021-02-23T11:16:31+01:00","dateModified":"2021-02-23T11:16:31+01:00","author":{"@type":"Person","name":"Leonhard Hennig"},"publisher":{"@type":"Organization","name":"DFKI-NLP","logo":{"@type":"ImageObject","url":"https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_192x192_fill_lanczos_center_2.png"}},"description":"In order to optimally prepare industry, science and the society in Germany and Europe for the global Big Data trend, highly coordinated activities in research, teaching, and technology transfer regarding the integration of data analysis methods and scalable data processing are required. 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 <span><i class="fas fa-bars"></i></span></button><div class="navbar-brand-mobile-wrapper d-inline-flex d-lg-none"><a class=navbar-brand href=../../>DFKI-NLP</a></div><div class="navbar-collapse main-menu-item collapse justify-content-end" id=navbar-content><ul class="navbar-nav d-md-inline-flex"><li class=nav-item><a class=nav-link href=../../#about><span>Home</span></a></li><li class=nav-item><a class=nav-link href=../../#news><span>News</span></a></li><li class=nav-item><a class=nav-link href=../../#people><span>People</span></a></li><li class=nav-item><a class=nav-link href=../../#publications><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=../../#projects><span>Projects</span></a></li><li class=nav-item><a class=nav-link href=../../#datasets><span>Datasets</span></a></li><li class=nav-item><a class=nav-link href=../../#contact><span>Contact</span></a></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li><li class="nav-item dropdown theme-dropdown"><a href=# class=nav-link data-toggle=dropdown aria-haspopup=true aria-label="Display preferences"><i class="fas fa-moon" aria-hidden=true></i></a><div class=dropdown-menu><a href=# class="dropdown-item js-set-theme-light"><span>Light</span></a>
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-<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>BBDC2</h1><div class=article-metadata><div><span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span></div><span class=article-date>Feb 23, 2021</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/bbdcii target=_blank rel=noopener>Go to Project Site</a></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:207px;max-height:79px><div style=position:relative><img src=../../project/bbdc2/featured.png alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/sim3s/>SIM3S</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/acl-srw2020-harbecke-considering/>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</a></div><a href=../../publication/acl-srw2020-harbecke-considering/ class=summary-link><div class=article-style>Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/david-harbecke/>David Harbecke</a></span>, <span><a href=../../authors/christoph-alt/>Christoph Alt</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://www.aclweb.org/anthology/2020.acl-srw.16.pdf target=_blank rel=noopener>PDF</a>
+<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>BBDC2</h1><div class=article-metadata><div><span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span></div><span class=article-date>Feb 23, 2021</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/bbdcii target=_blank rel=noopener>Go to Project Site</a></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:207px;max-height:79px><div style=position:relative><img src=../../project/bbdc2/featured.png alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/deeplee/>DEEPLEE</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/acl-srw2020-harbecke-considering/>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</a></div><a href=../../publication/acl-srw2020-harbecke-considering/ class=summary-link><div class=article-style>Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/david-harbecke/>David Harbecke</a></span>, <span><a href=../../authors/christoph-alt/>Christoph Alt</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://www.aclweb.org/anthology/2020.acl-srw.16.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/acl-srw2020-harbecke-considering/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/bbdc2/>Project</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.18653/v1/2020.acl-srw.16 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3></div></div><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/acl2020-alt-probing/>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</a></div><a href=../../publication/acl2020-alt-probing/ class=summary-link><div class=article-style>Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/christoph-alt/>Christoph Alt</a></span>, <span><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://www.aclweb.org/anthology/2020.acl-main.140.pdf target=_blank rel=noopener>PDF</a>
@@ -20,7 +20,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/swisstext2020-zaczynska-evaluating/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/bbdc2/>Project</a></div></div><div class=ml-3></div></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/project/bifold/index.html b/project/bifold/index.html
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 <!doctype html><html lang=en-us><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=generator content="Wowchemy 5.0.0-beta.2 for Hugo"><meta name=author content="DFKI-NLP"><meta name=description content="BIFOLD conducts foundational research in big data management and machine learning, as well as its intersection, to educate future talent, and create high-impact knowledge exchange. The Berlin Institute for the Foundations of Learning and Data (BIFOLD), has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the vibrant Berlin metropolitan area, BIFOLD provides an outstanding scientific environment and numerous collaboration opportunities for national and international researchers. BIFOLD offers a broad range of research topics as well as a platform for interdisciplinary research and knowledge exchange with the sciences and humanities, industry, startups and society. Within BIFOLD, DFKI SLT conducts research in Clinical AI, specifically addressing the task of Pharmacovigilance. Pharmacovigilance is concerned with the assessment and prevention of adverse drug reactions (ADR) in pharmaceutical products. As the level of medication is generally raising all over the world, the potential risk of unwanted side effects, such as ADRs, is constantly increasing. Patients exchange views in their own language as 'experts in their own right,' in social media and disease-specific forums. Our project addresses the detection and extraction of ADR from medical forums and social media across different languages using cross-lingual transfer learning in combination with external knowledge sources."><link rel=alternate hreflang=en-us href=https://dfki-nlp.github.io/project/bifold/><link rel=preconnect href=https://fonts.gstatic.com crossorigin><meta name=theme-color content="#3f51b5"><script src=../../js/mathjax-config.js></script><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/academicons/1.9.0/css/academicons.min.css integrity="sha512-W4yqoT1+8NLkinBLBZko+dFB2ZbHsYLDdr50VElllRcNt2Q4/GSs6u71UHKxB7S6JEMCp5Ve4xjh3eGQl/HRvg==" crossorigin=anonymous><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.14.0/css/all.min.css integrity="sha256-FMvZuGapsJLjouA6k7Eo2lusoAX9i0ShlWFG6qt7SLc=" crossorigin=anonymous><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/fancybox/3.5.7/jquery.fancybox.min.css integrity="sha256-Vzbj7sDDS/woiFS3uNKo8eIuni59rjyNGtXfstRzStA=" crossorigin=anonymous media=print onload="this.media='all'"><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.2.0/styles/github.min.css crossorigin=anonymous title=hl-light media=print onload="this.media='all'"><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.2.0/styles/dracula.min.css crossorigin=anonymous title=hl-dark media=print onload="this.media='all'" disabled><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.7.1/leaflet.min.css integrity="sha512-1xoFisiGdy9nvho8EgXuXvnpR5GAMSjFwp40gSRE3NwdUdIMIKuPa7bqoUhLD0O/5tPNhteAsE5XyyMi5reQVA==" crossorigin=anonymous media=print onload="this.media='all'"><script src=https://cdnjs.cloudflare.com/ajax/libs/lazysizes/5.2.2/lazysizes.min.js integrity="sha512-TmDwFLhg3UA4ZG0Eb4MIyT1O1Mb+Oww5kFG0uHqXsdbyZz9DcvYQhKpGgNkamAI6h2lGGZq2X8ftOJvF/XjTUg==" crossorigin=anonymous async></script><script src=https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js integrity crossorigin=anonymous async></script><link rel=preload as=style href="https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700&family=Roboto+Mono&family=Roboto:wght@400;700&display=swap"><link rel=stylesheet href="https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700&family=Roboto+Mono&family=Roboto:wght@400;700&display=swap" media=print onload="this.media='all'"><link rel=stylesheet href=../../css/wowchemy.0abaadd3907f09eb28431199a31e5765.css><link rel=manifest href=../../index.webmanifest><link rel=icon type=image/png href=../../images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_32x32_fill_lanczos_center_2.png><link rel=apple-touch-icon type=image/png href=../../images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_180x180_fill_lanczos_center_2.png><link rel=canonical href=https://dfki-nlp.github.io/project/bifold/><meta property="twitter:card" content="summary_large_image"><meta property="og:site_name" content="DFKI-NLP"><meta property="og:url" content="https://dfki-nlp.github.io/project/bifold/"><meta property="og:title" content="BIFOLD | DFKI-NLP"><meta property="og:description" content="BIFOLD conducts foundational research in big data management and machine learning, as well as its intersection, to educate future talent, and create high-impact knowledge exchange. The Berlin Institute for the Foundations of Learning and Data (BIFOLD), has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the vibrant Berlin metropolitan area, BIFOLD provides an outstanding scientific environment and numerous collaboration opportunities for national and international researchers. BIFOLD offers a broad range of research topics as well as a platform for interdisciplinary research and knowledge exchange with the sciences and humanities, industry, startups and society. Within BIFOLD, DFKI SLT conducts research in Clinical AI, specifically addressing the task of Pharmacovigilance. Pharmacovigilance is concerned with the assessment and prevention of adverse drug reactions (ADR) in pharmaceutical products. As the level of medication is generally raising all over the world, the potential risk of unwanted side effects, such as ADRs, is constantly increasing. Patients exchange views in their own language as 'experts in their own right,' in social media and disease-specific forums. Our project addresses the detection and extraction of ADR from medical forums and social media across different languages using cross-lingual transfer learning in combination with external knowledge sources."><meta property="og:image" content="https://dfki-nlp.github.io/project/bifold/featured.png"><meta property="twitter:image" content="https://dfki-nlp.github.io/project/bifold/featured.png"><meta property="og:locale" content="en-us"><meta property="article:published_time" content="2022-01-20T11:16:31+01:00"><meta property="article:modified_time" content="2022-01-20T11:16:31+01:00"><script type=application/ld+json>{"@context":"https://schema.org","@type":"Article","mainEntityOfPage":{"@type":"WebPage","@id":"https://dfki-nlp.github.io/project/bifold/"},"headline":"BIFOLD","image":["https://dfki-nlp.github.io/project/bifold/featured.png"],"datePublished":"2022-01-20T11:16:31+01:00","dateModified":"2022-01-20T11:16:31+01:00","author":{"@type":"Person","name":"Philippe Thomas"},"publisher":{"@type":"Organization","name":"DFKI-NLP","logo":{"@type":"ImageObject","url":"https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_192x192_fill_lanczos_center_2.png"}},"description":"BIFOLD conducts foundational research in big data management and machine learning, as well as its intersection, to educate future talent, and create high-impact knowledge exchange. The Berlin Institute for the Foundations of Learning and Data (BIFOLD), has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the vibrant Berlin metropolitan area, BIFOLD provides an outstanding scientific environment and numerous collaboration opportunities for national and international researchers. BIFOLD offers a broad range of research topics as well as a platform for interdisciplinary research and knowledge exchange with the sciences and humanities, industry, startups and society. Within BIFOLD, DFKI SLT conducts research in Clinical AI, specifically addressing the task of Pharmacovigilance. Pharmacovigilance is concerned with the assessment and prevention of adverse drug reactions (ADR) in pharmaceutical products. As the level of medication is generally raising all over the world, the potential risk of unwanted side effects, such as ADRs, is constantly increasing. Patients exchange views in their own language as 'experts in their own right,' in social media and disease-specific forums. Our project addresses the detection and extraction of ADR from medical forums and social media across different languages using cross-lingual transfer learning in combination with external knowledge sources."}</script><script src=https://cdnjs.cloudflare.com/ajax/libs/cookieconsent2/3.1.1/cookieconsent.min.js integrity="sha256-5VhCqFam2Cn+yjw61zbBNrbHVJ6SRydPeKopYlngbiQ=" crossorigin=anonymous></script><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/cookieconsent2/3.1.1/cookieconsent.min.css integrity="sha256-zQ0LblD/Af8vOppw18+2anxsuaz3pWYyVWi+bTvTH8Q=" crossorigin=anonymous><script>window.addEventListener("load",function(){window.cookieconsent.initialise({palette:{popup:{background:"#3f51b5",text:"rgb(255, 255, 255)"},button:{background:"rgb(255, 255, 255)",text:"#3f51b5"}},theme:"classic",content:{message:"This website uses cookies to ensure you get the best experience on our website.",dismiss:"Got it!",link:"Learn more",href:"/privacy/"}})})</script><title>BIFOLD | DFKI-NLP</title></head><body id=top data-spy=scroll data-offset=70 data-target=#TableOfContents class=page-wrapper><script src=../../js/wowchemy-init.min.2e458e5393490f06d99da74a463a188b.js></script><aside class=search-modal id=search><div class=container><section class=search-header><div class="row no-gutters justify-content-between mb-3"><div class=col-6><h1>Search</h1></div><div class="col-6 col-search-close"><a class=js-search href=# aria-label=Close><i class="fas fa-times-circle text-muted" aria-hidden=true></i></a></div></div><div id=search-box><input name=q id=search-query placeholder=Search... autocapitalize=off autocomplete=off autocorrect=off spellcheck=false type=search class=form-control aria-label=Search...></div></section><section class=section-search-results><div id=search-hits></div></section></div></aside><div class=page-header><nav class="navbar navbar-expand-lg navbar-light compensate-for-scrollbar" id=navbar-main><div class=container-xl><div class="d-none d-lg-inline-flex"><a class=navbar-brand href=../../>DFKI-NLP</a></div><button type=button class=navbar-toggler data-toggle=collapse data-target=#navbar-content aria-controls=navbar aria-expanded=false aria-label="Toggle navigation">
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+<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>BIFOLD</h1><div class=article-metadata><div><span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span></div><span class=article-date>Jan 20, 2022</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://bifold.berlin/de/ target=_blank rel=noopener>Go to Project Site</a></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:382px;max-height:93px><div style=position:relative><img src=../../project/bifold/featured.png alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a></div><div class="media author-card content-widget-hr"><a href=../../authors/philippe-thomas/><img class="avatar mr-3 avatar-circle" src=../../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt="Philippe Thomas"></a><div class=media-body><h5 class=card-title><a href=../../authors/philippe-thomas/>Philippe Thomas</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/bbdc2/>BBDC2</a></li><li><a href=../../project/deeplee/>DEEPLEE</a></li></ul></div><div class="project-related-pages content-widget-hr"></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/project/cora4nlp/index.html b/project/cora4nlp/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>Cora4NLP</h1><div class=article-metadata><div><span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/christoph-alt/>Christoph Alt</a></span></div><span class=article-date>Feb 23, 2021</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://cora4nlp.github.io target=_blank rel=noopener>Go to Project Site</a></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:244px;max-height:170px><div style=position:relative><img src=../../project/cora4nlp/featured.jpg alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a>
-<a class="badge badge-light" href=../../tag/language-understanding/>Language Understanding</a></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/deeplee/>DEEPLEE</a></li><li><a href=../../dataset/german-patient-adr/>German Adverse Drug Reaction (ADR) detection in patient-generated content</a></li><li><a href=../../dataset/dfki-mobasa-corpus/>MobASA Corpus</a></li><li><a href=../../dataset/dfki-mobie-corpus/>MobIE Corpus</a></li><li><a href=../../dataset/dfki-product-corpus/>Product Corpus</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/konvens2023-binsumait-etal-factuality/>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</a></div><a href=../../publication/konvens2023-binsumait-etal-factuality/ class=summary-link><div class=article-style>Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/mohammed-bin-sumait/>Mohammed Bin Sumait</a></span>, <span><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/roland-roller/>Roland Roller</a></span></div></div><div class=btn-links><a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/konvens2023-binsumait-etal-factuality/cite.bib>Cite</a>
+<a class="badge badge-light" href=../../tag/language-understanding/>Language Understanding</a></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/deeplee/>DEEPLEE</a></li><li><a href=../../dataset/german-patient-adr/>German Adverse Drug Reaction (ADR) detection in patient-generated content</a></li><li><a href=../../dataset/dfki-mobasa-corpus/>MobASA Corpus</a></li><li><a href=../../dataset/dfki-mobie-corpus/>MobIE Corpus</a></li><li><a href=../../dataset/dfki-smartdata-corpus/>SmartData Corpus</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/konvens2023-binsumait-etal-factuality/>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</a></div><a href=../../publication/konvens2023-binsumait-etal-factuality/ class=summary-link><div class=article-style>Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/mohammed-bin-sumait/>Mohammed Bin Sumait</a></span>, <span><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/roland-roller/>Roland Roller</a></span></div></div><div class=btn-links><a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/konvens2023-binsumait-etal-factuality/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/cora4nlp/>Project</a></div></div><div class=ml-3></div></div><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/acl2023-hennig-multitacred/>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</a></div><a href=../../publication/acl2023-hennig-multitacred/ class=summary-link><div class=article-style>Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span>, <span><a href=../../authors/sebastian-moller/>Sebastian Möller</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://arxiv.org/pdf/2305.04582.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/acl2023-hennig-multitacred/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/DFKI-NLP/MultiTACRED target=_blank rel=noopener>Code</a>
@@ -18,7 +18,7 @@
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/cora4nlp/>Project</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.48550/arXiv.2210.13838 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3></div></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/project/data4transparency/index.html b/project/data4transparency/index.html
index 18f4f9e9..2336885f 100644
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@@ -2,9 +2,9 @@
 <span><i class="fas fa-bars"></i></span></button><div class="navbar-brand-mobile-wrapper d-inline-flex d-lg-none"><a class=navbar-brand href=../../>DFKI-NLP</a></div><div class="navbar-collapse main-menu-item collapse justify-content-end" id=navbar-content><ul class="navbar-nav d-md-inline-flex"><li class=nav-item><a class=nav-link href=../../#about><span>Home</span></a></li><li class=nav-item><a class=nav-link href=../../#news><span>News</span></a></li><li class=nav-item><a class=nav-link href=../../#people><span>People</span></a></li><li class=nav-item><a class=nav-link href=../../#publications><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=../../#projects><span>Projects</span></a></li><li class=nav-item><a class=nav-link href=../../#datasets><span>Datasets</span></a></li><li class=nav-item><a class=nav-link href=../../#contact><span>Contact</span></a></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li><li class="nav-item dropdown theme-dropdown"><a href=# class=nav-link data-toggle=dropdown aria-haspopup=true aria-label="Display preferences"><i class="fas fa-moon" aria-hidden=true></i></a><div class=dropdown-menu><a href=# class="dropdown-item js-set-theme-light"><span>Light</span></a>
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>Data4Transparency</h1><div class=article-metadata><div><span><a href=../../authors/steffen-castle/>Steffen Castle</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span></div><span class=article-date>Apr 1, 2023</span></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:400px;max-height:80px><div style=position:relative><img src=../../project/data4transparency/featured.png alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a>
-<a class="badge badge-light" href=../../tag/low-resource-learning/>Low-Resource Learning</a></div><div class="media author-card content-widget-hr"><a href=../../authors/steffen-castle/><img class="avatar mr-3 avatar-circle" src=../../authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Steffen Castle"></a><div class=media-body><h5 class=card-title><a href=../../authors/steffen-castle/>Steffen Castle</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../project/plass/>PLASS</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/deeplee/>DEEPLEE</a></li></ul></div><div class="project-related-pages content-widget-hr"></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
+<a class="badge badge-light" href=../../tag/low-resource-learning/>Low-Resource Learning</a></div><div class="media author-card content-widget-hr"><a href=../../authors/steffen-castle/><img class="avatar mr-3 avatar-circle" src=../../authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Steffen Castle"></a><div class=media-body><h5 class=card-title><a href=../../authors/steffen-castle/>Steffen Castle</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../project/plass/>PLASS</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/sim3s/>SIM3S</a></li></ul></div><div class="project-related-pages content-widget-hr"></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/project/deeplee/index.html b/project/deeplee/index.html
index 0cf5f15c..b798952c 100644
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@@ -16,7 +16,7 @@
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/plass/>Project</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.18653/v1/2020.acl-main.142 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3></div></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/project/index.html b/project/index.html
index 439f11d6..fb06561f 100644
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@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Projects</h1></div><div class=universal-wrapper><div><h2><a href=../project/data4transparency/>Data4Transparency</a></h2><div class=article-style>According to the World Bank and the UN, some US$1tn is paid in bribes every year. Corrupt financial transactions divert funds from legitimate public services, as well as distort free markets—potentially thwarting economic development—and reduce trust in institutions. The Organized Crime and Corruption Reporting Project (OCCRP) is a global platform for investigative reporting, providing resources to journalists and media centres, enabling cost-effective collaboration between editors and offering tools to secure themselves against threats to independent media. Exposing previously-unknown connections between entities makes it possible for citizens, policymakers, activists and law enforcement agencies to act. As the number of such leaks and publications grows, there is an increasing need for effective, scalable and reproducible methods to discover any anomalies and evidence of malfeasance that might exist within them.</div></div><div><h2><a href=https://www.text2tech.de target=_blank rel=noopener>Text2Tech</a></h2><div class=article-style>The goal of the Text2Tech project is the research and development of automated methods for information extraction from unstructured text sources in order to be able to provide companies with decision-relevant knowledge about technological developments quickly and efficiently. AI-based methods for information extraction (IE) already make it possible to extract selected information, e.g. B. to people, companies and places automatically from text sources. In the Text2Tech project, such approaches are to be further developed in order to extract machine-readable knowledge about technologies, technology categories, companies and their relationships with each other from German and English-language, domain-specific text sources, using the example of the automotive industry. The most important research goals are the modeling and filling of domain-specific knowledge graphs (Knowledge Base Population), the development of methods for cross-lingual proper name recognition and linking (Named Entity Recognition or Entity Linking), relation extraction (Relation Extraction), as well as the development of Model compression methods so that models run efficiently even on small hardware.</div></div><div><h2><a href=https://bifold.berlin/de/ target=_blank rel=noopener>BIFOLD</a></h2><div class=article-style>BIFOLD conducts foundational research in big data management and machine learning, as well as its intersection, to educate future talent, and create high-impact knowledge exchange. The Berlin Institute for the Foundations of Learning and Data (BIFOLD), has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the vibrant Berlin metropolitan area, BIFOLD provides an outstanding scientific environment and numerous collaboration opportunities for national and international researchers. BIFOLD offers a broad range of research topics as well as a platform for interdisciplinary research and knowledge exchange with the sciences and humanities, industry, startups and society. Within BIFOLD, DFKI SLT conducts research in Clinical AI, specifically addressing the task of Pharmacovigilance. Pharmacovigilance is concerned with the assessment and prevention of adverse drug reactions (ADR) in pharmaceutical products. As the level of medication is generally raising all over the world, the potential risk of unwanted side effects, such as ADRs, is constantly increasing. Patients exchange views in their own language as 'experts in their own right,' in social media and disease-specific forums. Our project addresses the detection and extraction of ADR from medical forums and social media across different languages using cross-lingual transfer learning in combination with external knowledge sources.</div></div><div><h2><a href=https://cora4nlp.github.io target=_blank rel=noopener>Cora4NLP</a></h2><div class=article-style>Language is implicit - it omits information. Filling this information gap requires contextual inference, background- and commonsense knowledge, and reasoning over situational context. Language also evolves, i.e., it specializes and changes over time. For example, many different languages and domains exist, new domains arise, and both evolve constantly. Thus, language understanding also requires continuous and efficient adaptation to new languages and domains, and transfer to, and between, both. Current language understanding technology, however, focuses on high resource languages and domains, uses little to no context, and assumes static data, task, and target distributions. The research in Cora4NLP aims to address these challenges. It builds on the expertise and results of the predecessor project DEEPLEE and is carried out jointly between the language technology research departments in Berlin and Saarbrücken.</div></div><div><h2><a href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/bbdcii target=_blank rel=noopener>BBDC2</a></h2><div class=article-style>In order to optimally prepare industry, science and the society in Germany and Europe for the global Big Data trend, highly coordinated activities in research, teaching, and technology transfer regarding the integration of data analysis methods and scalable data processing are required. To achieve this, the Berlin Big Data Center is pursuing the following seven objectives: 1) Pooling expertise in scalable data management, data analytics, and big data application 2) Conducting fundamental research to develop novel and automatically scalable technologies capable of performing 'Deep Analysis' of 'Big Data'. 3) Developing an integrated, declarative, highly scalable open-source system that enables the specification, automatic optimization, parallelization and hardware adaptation, and fault-tolerant, efficient execution of advanced data analysis problems, using varying methods (e.g., drawn from machine learning, linear algebra, statistics and probability theory, computational linguistics, or signal processing), leveraging our work on Apache Flink 4) Transfering technology and know-how to support innovation in companies and startups. 5) Educating data scientists with respect to the five big data dimensions (i.e., applications, economic, legal, social, and technological) via leading educational programs. 6) Empowering people to leverage 'Smart Data', i.e., to discover newfound information based on their massive data sets. 7)Enabling the general public to conduct sound data-driven decision-making.</div></div><div><h2><a href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/deeplee target=_blank rel=noopener>DEEPLEE</a></h2><div class=article-style>The research work in DEEPLEE, which is carried out in the Language Technology research departments in Saabrücken and Berlin, builds on DFKI's expertise in the areas of deep learning (DL) and language technology (LT) and develops it further. They aim for profound improvements of DL approaches in LT by focusing on four central, open research topics: Modularity in DNN architectures, Use of external knowledge, DNNs with explanation functionality, Machine Teaching Strategies for DNNs</div></div><div><h2><a href=https://plass.io target=_blank rel=noopener>PLASS</a></h2><div class=article-style>The aim of the PLASS project is to develop a prototypical B2B platform for AI-based decision support for supply chain management. The focus is on the automatic recognition of decision-relevant information and the acquisition of structured knowledge from global and multilingual text sources. These sources provide a large database for SCM information, especially for the early detection of critical events and risks, but also of opportunities, e.g. through new technologies, at suppliers and supply chains. PLASS enables SMEs and large companies to continuously monitor their suppliers and supply chains, and supports supply chain managers in risk assessment and decision-making.</div></div><div><h2><a href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/sim3s/ target=_blank rel=noopener>SIM3S</a></h2><div class=article-style>In the SIM3S project, data from the BMVI data offerings mCloud and MDM will be linked, refined and jointly analysed with other open data, user-generated content and data from individual modes of transport and other mobility-relevant companies in order to remove barriers and barriers to discrimination in everyday mobility. For the implementation of the project, state-of-the-art technologies and methods from the areas of Big Data Intelligent Analysis of mass data and artificial intelligence, in particular Natural Language Processing (NLP), are used.</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
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diff --git a/project/index.xml b/project/index.xml
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Projects | DFKI-NLP</title><link>https://dfki-nlp.github.io/project/</link><atom:link href="https://dfki-nlp.github.io/project/index.xml" rel="self" type="application/rss+xml"/><description>Projects</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Sat, 01 Apr 2023 11:16:31 +0100</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Projects</title><link>https://dfki-nlp.github.io/project/</link></image><item><title>Data4Transparency</title><link>https://dfki-nlp.github.io/project/data4transparency/</link><pubDate>Sat, 01 Apr 2023 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/data4transparency/</guid><description/></item><item><title>Text2Tech</title><link>https://dfki-nlp.github.io/project/text2tech/</link><pubDate>Wed, 01 Jun 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/text2tech/</guid><description/></item><item><title>BIFOLD</title><link>https://dfki-nlp.github.io/project/bifold/</link><pubDate>Thu, 20 Jan 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/bifold/</guid><description/></item><item><title>Cora4NLP</title><link>https://dfki-nlp.github.io/project/cora4nlp/</link><pubDate>Tue, 23 Feb 2021 11:23:02 +0100</pubDate><guid>https://dfki-nlp.github.io/project/cora4nlp/</guid><description/></item><item><title>BBDC2</title><link>https://dfki-nlp.github.io/project/bbdc2/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/bbdc2/</guid><description/></item><item><title>DEEPLEE</title><link>https://dfki-nlp.github.io/project/deeplee/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/deeplee/</guid><description/></item><item><title>PLASS</title><link>https://dfki-nlp.github.io/project/plass/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/plass/</guid><description/></item><item><title>SIM3S</title><link>https://dfki-nlp.github.io/project/sim3s/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/sim3s/</guid><description/></item></channel></rss>
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diff --git a/project/plass/index.html b/project/plass/index.html
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 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.18653/v1/2020.acl-main.142 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3></div></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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-<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>SIM3S</h1><div class=article-metadata><div><span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span></div><span class=article-date>Feb 23, 2021</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/sim3s/ target=_blank rel=noopener>Go to Project Site</a></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:300px;max-height:130px><div style=position:relative><img src=../../project/sim3s/featured.png alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a></div><div class="media author-card content-widget-hr"><a href=../../authors/philippe-thomas/><img class="avatar mr-3 avatar-circle" src=../../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt="Philippe Thomas"></a><div class=media-body><h5 class=card-title><a href=../../authors/philippe-thomas/>Philippe Thomas</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/bbdc2/>BBDC2</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/lrec2022-gabryszak/>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</a></div><a href=../../publication/lrec2022-gabryszak/ class=summary-link><div class=article-style>In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></span>, <span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=http://www.lrec-conf.org/proceedings/lrec2022/workshops/CSRNLP1/pdf/2022.csrnlp1-1.5.pdf target=_blank rel=noopener>PDF</a>
+<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>SIM3S</h1><div class=article-metadata><div><span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span></div><span class=article-date>Feb 23, 2021</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/sim3s/ target=_blank rel=noopener>Go to Project Site</a></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:300px;max-height:130px><div style=position:relative><img src=../../project/sim3s/featured.png alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a></div><div class="media author-card content-widget-hr"><a href=../../authors/philippe-thomas/><img class="avatar mr-3 avatar-circle" src=../../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt="Philippe Thomas"></a><div class=media-body><h5 class=card-title><a href=../../authors/philippe-thomas/>Philippe Thomas</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../project/text2tech/>Text2Tech</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/plass/>PLASS</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/lrec2022-gabryszak/>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</a></div><a href=../../publication/lrec2022-gabryszak/ class=summary-link><div class=article-style>In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></span>, <span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=http://www.lrec-conf.org/proceedings/lrec2022/workshops/CSRNLP1/pdf/2022.csrnlp1-1.5.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/lrec2022-gabryszak/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/sim3s/>Project</a></div></div><div class=ml-3></div></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/project/text2tech/index.html b/project/text2tech/index.html
index 33be452d..02800515 100644
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@@ -2,7 +2,7 @@
 <span><i class="fas fa-bars"></i></span></button><div class="navbar-brand-mobile-wrapper d-inline-flex d-lg-none"><a class=navbar-brand href=../../>DFKI-NLP</a></div><div class="navbar-collapse main-menu-item collapse justify-content-end" id=navbar-content><ul class="navbar-nav d-md-inline-flex"><li class=nav-item><a class=nav-link href=../../#about><span>Home</span></a></li><li class=nav-item><a class=nav-link href=../../#news><span>News</span></a></li><li class=nav-item><a class=nav-link href=../../#people><span>People</span></a></li><li class=nav-item><a class=nav-link href=../../#publications><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=../../#projects><span>Projects</span></a></li><li class=nav-item><a class=nav-link href=../../#datasets><span>Datasets</span></a></li><li class=nav-item><a class=nav-link href=../../#contact><span>Contact</span></a></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li><li class="nav-item dropdown theme-dropdown"><a href=# class=nav-link data-toggle=dropdown aria-haspopup=true aria-label="Display preferences"><i class="fas fa-moon" aria-hidden=true></i></a><div class=dropdown-menu><a href=# class="dropdown-item js-set-theme-light"><span>Light</span></a>
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class="article article-project"><div class="article-container pt-3"><h1>Text2Tech</h1><div class=article-metadata><div><span><a href=../../authors/david-harbecke/>David Harbecke</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span></div><span class=article-date>Jun 1, 2022</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary my-1" href=https://www.text2tech.de target=_blank rel=noopener>Go to Project Site</a></div></div><div class="article-header article-container featured-image-wrapper mt-4 mb-4" style=max-width:720px;max-height:327px><div style=position:relative><img src=../../project/text2tech/featured_hud72c98d9abfc553b147f72aefd5a9c16_151052_720x0_resize_lanczos_2.png alt class=featured-image></div></div><div class=article-container><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/information-extraction/>Information Extraction</a>
-<a class="badge badge-light" href=../../tag/low-resource-learning/>Low-Resource Learning</a></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../project/plass/>PLASS</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/sim3s/>SIM3S</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/acl2023-hennig-multitacred/>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</a></div><a href=../../publication/acl2023-hennig-multitacred/ class=summary-link><div class=article-style>Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span>, <span><a href=../../authors/sebastian-moller/>Sebastian Möller</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://arxiv.org/pdf/2305.04582.pdf target=_blank rel=noopener>PDF</a>
+<a class="badge badge-light" href=../../tag/low-resource-learning/>Low-Resource Learning</a></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../project/data4transparency/>Data4Transparency</a></li><li><a href=../../project/plass/>PLASS</a></li><li><a href=../../project/bifold/>BIFOLD</a></li><li><a href=../../project/cora4nlp/>Cora4NLP</a></li><li><a href=../../project/bbdc2/>BBDC2</a></li></ul></div><div class="project-related-pages content-widget-hr"><h2>Publications</h2><div class="media stream-item"><div class=media-body><div class="section-subheading article-title mb-0 mt-0"><a href=../../publication/acl2023-hennig-multitacred/>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</a></div><a href=../../publication/acl2023-hennig-multitacred/ class=summary-link><div class=article-style>Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by …</div></a><div class="stream-meta article-metadata"><div><span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span>, <span><a href=../../authors/sebastian-moller/>Sebastian Möller</a></span></div></div><div class=btn-links><a class="btn btn-outline-primary btn-page-header btn-sm" href=https://arxiv.org/pdf/2305.04582.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/acl2023-hennig-multitacred/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/DFKI-NLP/MultiTACRED target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/cora4nlp/>Project</a>
@@ -10,7 +10,7 @@
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=../../project/text2tech/>Project</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://doi.org/10.48550/arXiv.2305.04582 target=_blank rel=noopener>DOI</a></div></div><div class=ml-3></div></div></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication-type/1/index.xml b/publication-type/1/index.xml
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@@ -1 +1 @@
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+0000</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</guid><description/></item><item><title>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</title><link>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</link><pubDate>Thu, 17 Aug 2023 08:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</guid><description/></item><item><title>Inseq: An Interpretability Toolkit for Sequence Generation Models</title><link>https://dfki-nlp.github.io/publication/acl2023-sarti-inseq/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-sarti-inseq/</guid><description/></item><item><title>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</title><link>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</guid><description/></item><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning</title><link>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</link><pubDate>Tue, 16 May 2023 09:39:26 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</guid><description/></item><item><title>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</title><link>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</guid><description/></item><item><title>VendorLink: An NLP approach for Identifying &amp; Linking Vendor Migrants &amp; Potential Aliases on Darknet Markets</title><link>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</guid><description/></item><item><title>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</title><link>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</guid><description/></item><item><title>Multilingual Relation Classification via Efficient and Effective Prompting</title><link>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</link><pubDate>Sat, 22 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</guid><description/></item><item><title>Full-Text Argumentation Mining on Scientific Publications</title><link>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</link><pubDate>Fri, 21 Oct 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</guid><description/></item><item><title>Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings</title><link>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</link><pubDate>Fri, 14 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</guid><description/></item><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item><item><title>An Annotated Corpus of Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/publication/lrec2022-roller/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-roller/</guid><description/></item><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item><item><title>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</guid><description/></item><item><title>Subjective Text Complexity Assessment for German</title><link>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</guid><description/></item><item><title>Claim Extraction and Law Matching for COVID-19-related Legislation</title><link>https://dfki-nlp.github.io/publication/lrec2022-dehio/</link><pubDate>Wed, 06 Jul 2022 09:37:49 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-dehio/</guid><description/></item><item><title>Specialized Document Embeddings for Aspect-based Similarity of Research Papers</title><link>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</link><pubDate>Wed, 06 Jul 2022 09:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</guid><description/></item><item><title>HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information</title><link>https://dfki-nlp.github.io/publication/acl2022-ruan/</link><pubDate>Wed, 06 Jul 2022 09:25:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-ruan/</guid><description/></item><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item><item><title>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</title><link>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</guid><description/></item><item><title>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</title><link>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</guid><description/></item><item><title>Detecting Covariate Drift with Explanations</title><link>https://dfki-nlp.github.io/publication/nlpcc-castle/</link><pubDate>Fri, 15 Oct 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/nlpcc-castle/</guid><description/></item><item><title>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</link><pubDate>Tue, 07 Sep 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</guid><description/></item><item><title>Evaluating Document Representations for Content-based Legal Literature Recommendations</title><link>https://dfki-nlp.github.io/publication/ostendorff2021/</link><pubDate>Thu, 27 May 2021 12:18:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2021/</guid><description/></item><item><title>Aspect-based Document Similarity for Research Papers</title><link>https://dfki-nlp.github.io/publication/ostendorff2020c/</link><pubDate>Sun, 27 Dec 2020 12:17:32 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020c/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles</title><link>https://dfki-nlp.github.io/publication/ostendorff2020/</link><pubDate>Thu, 27 Aug 2020 12:17:42 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020/</guid><description/></item><item><title>Pattern-Guided Integrated Gradients</title><link>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</link><pubDate>Wed, 29 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</guid><description/></item><item><title>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</title><link>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</guid><description/></item><item><title>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</title><link>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</guid><description/></item><item><title>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</guid><description/></item><item><title>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</guid><description/></item><item><title>Evaluating German Transformer Language Models with Syntactic Agreement Tests</title><link>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</link><pubDate>Tue, 23 Jun 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Cross-lingual Neural Vector Conceptualization</title><link>https://dfki-nlp.github.io/publication/cl-nvc/</link><pubDate>Mon, 30 Sep 2019 18:09:58 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/cl-nvc/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</link><pubDate>Mon, 29 Jul 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</guid><description/></item><item><title>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</title><link>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</link><pubDate>Mon, 24 Jun 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</guid><description/></item><item><title>Enriching BERT with Knowledge Graph Embedding for Document Classification</title><link>https://dfki-nlp.github.io/publication/ostendorff2019/</link><pubDate>Mon, 27 May 2019 12:32:18 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2019/</guid><description/></item><item><title>Improving Relation Extraction by Pre-Trained Language Representations</title><link>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</link><pubDate>Mon, 20 May 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</guid><description/></item><item><title>Neural Vector Conceptualization for Word Vector Space Interpretation</title><link>https://dfki-nlp.github.io/publication/nvc/</link><pubDate>Tue, 02 Apr 2019 17:55:21 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/nvc/</guid><description/></item><item><title>Train, Sort, Explain: Learning to Diagnose Translation Models</title><link>https://dfki-nlp.github.io/publication/diamat/</link><pubDate>Thu, 28 Mar 2019 17:40:45 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/diamat/</guid><description/></item><item><title>mEx - an Information Extraction Platform for German Medical Text</title><link>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</link><pubDate>Sat, 01 Dec 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</guid><description/></item><item><title>Learning Explanations From Language Data</title><link>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</link><pubDate>Fri, 02 Nov 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</guid><description/></item></channel></rss>
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>1 | DFKI-NLP</title><link>https://dfki-nlp.github.io/publication-type/1/</link><atom:link href="https://dfki-nlp.github.io/publication-type/1/index.xml" rel="self" type="application/rss+xml"/><description>1</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 06 Dec 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>1</title><link>https://dfki-nlp.github.io/publication-type/1/</link></image><item><title>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</title><link>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</link><pubDate>Wed, 06 Dec 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</guid><description/></item><item><title>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</title><link>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</link><pubDate>Thu, 17 Aug 2023 08:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</guid><description/></item><item><title>Inseq: An Interpretability Toolkit for Sequence Generation Models</title><link>https://dfki-nlp.github.io/publication/acl2023-sarti-inseq/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-sarti-inseq/</guid><description/></item><item><title>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</title><link>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</guid><description/></item><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning</title><link>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</link><pubDate>Tue, 16 May 2023 09:39:26 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</guid><description/></item><item><title>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</title><link>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</guid><description/></item><item><title>VendorLink: An NLP approach for Identifying &amp; Linking Vendor Migrants &amp; Potential Aliases on Darknet Markets</title><link>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</guid><description/></item><item><title>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</title><link>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</guid><description/></item><item><title>Multilingual Relation Classification via Efficient and Effective Prompting</title><link>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</link><pubDate>Sat, 22 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</guid><description/></item><item><title>Full-Text Argumentation Mining on Scientific Publications</title><link>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</link><pubDate>Fri, 21 Oct 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</guid><description/></item><item><title>Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings</title><link>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</link><pubDate>Fri, 14 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</guid><description/></item><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item><item><title>An Annotated Corpus of Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/publication/lrec2022-roller/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-roller/</guid><description/></item><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item><item><title>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</guid><description/></item><item><title>Subjective Text Complexity Assessment for German</title><link>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</guid><description/></item><item><title>Claim Extraction and Law Matching for COVID-19-related Legislation</title><link>https://dfki-nlp.github.io/publication/lrec2022-dehio/</link><pubDate>Wed, 06 Jul 2022 09:37:49 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-dehio/</guid><description/></item><item><title>Specialized Document Embeddings for Aspect-based Similarity of Research Papers</title><link>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</link><pubDate>Wed, 06 Jul 2022 09:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</guid><description/></item><item><title>HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information</title><link>https://dfki-nlp.github.io/publication/acl2022-ruan/</link><pubDate>Wed, 06 Jul 2022 09:25:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-ruan/</guid><description/></item><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item><item><title>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</title><link>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</guid><description/></item><item><title>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</title><link>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</guid><description/></item><item><title>Detecting Covariate Drift with Explanations</title><link>https://dfki-nlp.github.io/publication/nlpcc-castle/</link><pubDate>Fri, 15 Oct 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/nlpcc-castle/</guid><description/></item><item><title>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</link><pubDate>Tue, 07 Sep 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</guid><description/></item><item><title>Evaluating Document Representations for Content-based Legal Literature Recommendations</title><link>https://dfki-nlp.github.io/publication/ostendorff2021/</link><pubDate>Thu, 27 May 2021 12:18:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2021/</guid><description/></item><item><title>Aspect-based Document Similarity for Research Papers</title><link>https://dfki-nlp.github.io/publication/ostendorff2020c/</link><pubDate>Sun, 27 Dec 2020 12:17:32 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020c/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles</title><link>https://dfki-nlp.github.io/publication/ostendorff2020/</link><pubDate>Thu, 27 Aug 2020 12:17:42 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020/</guid><description/></item><item><title>Pattern-Guided Integrated Gradients</title><link>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</link><pubDate>Wed, 29 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</guid><description/></item><item><title>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</title><link>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</guid><description/></item><item><title>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</title><link>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</guid><description/></item><item><title>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</guid><description/></item><item><title>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</guid><description/></item><item><title>Evaluating German Transformer Language Models with Syntactic Agreement Tests</title><link>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</link><pubDate>Tue, 23 Jun 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Cross-lingual Neural Vector Conceptualization</title><link>https://dfki-nlp.github.io/publication/cl-nvc/</link><pubDate>Mon, 30 Sep 2019 18:09:58 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/cl-nvc/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</link><pubDate>Mon, 29 Jul 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</guid><description/></item><item><title>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</title><link>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</link><pubDate>Mon, 24 Jun 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</guid><description/></item><item><title>Enriching BERT with Knowledge Graph Embedding for Document Classification</title><link>https://dfki-nlp.github.io/publication/ostendorff2019/</link><pubDate>Mon, 27 May 2019 12:32:18 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2019/</guid><description/></item><item><title>Improving Relation Extraction by Pre-Trained Language Representations</title><link>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</link><pubDate>Mon, 20 May 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</guid><description/></item><item><title>Neural Vector Conceptualization for Word Vector Space Interpretation</title><link>https://dfki-nlp.github.io/publication/nvc/</link><pubDate>Tue, 02 Apr 2019 17:55:21 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/nvc/</guid><description/></item><item><title>Train, Sort, Explain: Learning to Diagnose Translation Models</title><link>https://dfki-nlp.github.io/publication/diamat/</link><pubDate>Thu, 28 Mar 2019 17:40:45 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/diamat/</guid><description/></item><item><title>mEx - an Information Extraction Platform for German Medical Text</title><link>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</link><pubDate>Sat, 01 Dec 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</guid><description/></item><item><title>Learning Explanations From Language Data</title><link>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</link><pubDate>Fri, 02 Nov 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</guid><description/></item></channel></rss>
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>1</h1></div><div class=universal-wrapper><div><h2><a href=../../../../publication/wiesp2022-binder-etal-full/>Full-Text Argumentation Mining on Scientific Publications</a></h2><div class=article-style>Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and …</div></div><div><h2><a href=../../../../publication/emnlp2022-ostendorff/>Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings</a></h2><div class=article-style>Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics. Prior work …</div></div><div><h2><a href=../../../../publication/lrec2022-macketanz-testsuite/>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</a></h2><div class=article-style>This paper presents a fine-grained test suite for the language pair German–English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We …</div></div><div><h2><a href=../../../../publication/lrec2022-roller/>An Annotated Corpus of Textual Explanations for Clinical Decision Support</a></h2><div class=article-style>In recent years, machine learning for clinical decision support has gained more and more attention. In order to introduce such applications into clinical practice, a good performance might be essential, however, the aspect of trust should not be …</div></div><div><h2><a href=../../../../publication/lrec2022-raithel-cross-adr/>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get …</div></div><div><h2><a href=../../../../publication/lrec2022-calizzano/>Generating Extended and Multilingual Summaries with Pre-trained Transformers</a></h2><div class=article-style>Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended …</div></div><div><h2><a href=../../../../publication/lrec2022-gabryszak/>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</a></h2><div class=article-style>In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance …</div></div><div><h2><a href=../../../../publication/lrec2022-seiffe-subjective/>Subjective Text Complexity Assessment for German</a></h2><div class=article-style>For different reasons, text can be difficult to read and understand for many people, especially if the text’s language is too complex. In order to provide suitable text for the target audience, it is necessary to measure its complexity. In this paper …</div></div><div><h2><a href=../../../../publication/lrec2022-dehio/>Claim Extraction and Law Matching for COVID-19-related Legislation</a></h2><div class=article-style>To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what …</div></div><div><h2><a href=../../../../publication/jcdl2022-ostendorff/>Specialized Document Embeddings for Aspect-based Similarity of Research Papers</a></h2><div class=article-style>Document embeddings and similarity measures underpin content-based recommender systems, whereby a document is commonly represented as a single generic embedding. However, similarity computed on single vector representations provides only one …</div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../../../publication-type/1/>&#171;</a></li><li class=page-item><a class=page-link href=../../../../publication-type/1/page/3/>&#187;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../../../privacy/>Data Protection Notice</a>
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>1</h1></div><div class=universal-wrapper><div><h2><a href=../../../../publication/acl2022-ruan/>HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information</a></h2><div class=article-style>Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles …</div></div><div><h2><a href=../../../../publication/acl2022-humeval-macketanz-perceptual/>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</a></h2><div class=article-style>The quality of machine-generated text is a complex construct consisting of various aspects and dimensions. We present a study that aims to uncover relevant perceptual quality dimensions for one type of machine-generated text, that is, Machine …</div></div><div><h2><a href=../../../../publication/acl2022-repl4nlp-chen-fewie/>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</a></h2><div class=article-style>Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data. However, their …</div></div><div><h2><a href=../../../../publication/acl2022-nlppower-harbecke-f1/>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</a></h2><div class=article-style>Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce a framework for …</div></div><div><h2><a href=../../../../publication/nlpcc-castle/>Detecting Covariate Drift with Explanations</a></h2><div class=article-style>Detecting when there is a domain drift between training and inference data is important for any model evaluated on data collected in real time. Many current data drift detection methods only utilize input features to detect domain drift. While …</div></div><div><h2><a href=../../../../publication/konvens2021-hennig-mobie/>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</a></h2><div class=article-style>We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic …</div></div><div><h2><a href=../../../../publication/ostendorff2021/>Evaluating Document Representations for Content-based Legal Literature Recommendations</a></h2><div class=article-style>Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflect the latest advances in recommender systems and representation …</div></div><div><h2><a href=../../../../publication/ostendorff2020c/>Aspect-based Document Similarity for Research Papers</a></h2><div class=article-style>Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of applications like …</div></div><div><h2><a href=../../../../publication/semeval2020-huebner-defx/>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</a></h2><div class=article-style>We describe our submissions to the DeftEval shared task (SemEval-2020 Task 6)</div></div><div><h2><a href=../../../../publication/ostendorff2020/>Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles</a></h2><div class=article-style>Many digital libraries recommend literature to their users considering the similarity between a query document and their repository. However, they often fail to distinguish what is the relationship that makes two documents alike. In this paper, we …</div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../../../publication-type/1/page/2/>&#171;</a></li><li class=page-item><a class=page-link href=../../../../publication-type/1/page/4/>&#187;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../../../privacy/>Data Protection Notice</a>
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diff --git a/publication-type/1/page/4/index.html b/publication-type/1/page/4/index.html
index 43e4c88c..4b28f01d 100644
--- a/publication-type/1/page/4/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>1</h1></div><div class=universal-wrapper><div><h2><a href=../../../../publication/icml-schwarzenberg-castle/>Pattern-Guided Integrated Gradients</a></h2><div class=article-style>Integrated Gradients (IG) and PatternAttribution (PA) are two established explainability methods for neural networks. Both methods are theoretically well-founded. However, they were designed to overcome different challenges. In this work, we combine the two methods into a new method, Pattern-Guided Integrated Gradients (PGIG). PGIG inherits important properties from both parent methods and passes stress tests that the originals fail. In addition, we benchmark PGIG against nine alternative explainability approaches (including its parent methods) in a large-scale image degradation experiment and find that it outperforms all of them.</div></div><div><h2><a href=../../../../publication/acl-ecnlp2020-zhang-bootstrapping/>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</a></h2><div class=article-style>In this work, we introduce a bootstrapped, iterative NER model that integrates a PU learning algorithm for recognizing named entities in a low-resource setting. Our approach combines dictionary-based labeling with syntactically-informed label …</div></div><div><h2><a href=../../../../publication/acl-srw2020-harbecke-considering/>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</a></h2><div class=article-style>Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occlusion is a well established method that provides explanations on …</div></div><div><h2><a href=../../../../publication/acl2020-alt-probing/>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</a></h2><div class=article-style>Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, conditioned on the entity mentions, before classifying the relation. …</div></div><div><h2><a href=../../../../publication/acl2020-alt-tacred-revisited/>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</a></h2><div class=article-style>TACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training and knowledge enhanced neural RE, models still show a high error rate. In this paper, we …</div></div><div><h2><a href=../../../../publication/swisstext2020-zaczynska-evaluating/>Evaluating German Transformer Language Models with Syntactic Agreement Tests</a></h2><div class=article-style>Pre-trained transformer language models (TLMs) have recently refashioned natural language processing (NLP): Most stateof-the-art NLP models now operate on top of TLMs to benefit from contextualization and knowledge induction. To explain their …</div></div><div><h2><a href=../../../../publication/lrec2020-aksenov-abstractive/>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</a></h2><div class=article-style>We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on …</div></div><div><h2><a href=../../../../publication/cl-nvc/>Cross-lingual Neural Vector Conceptualization</a></h2><div class=article-style>Recently, Neural Vector Conceptualization (NVC) was proposed as a means to interpret samples from a word vector space. For NVC, a neural model activates higher order concepts it recognizes in a word vector instance. To this end, the model first needs …</div></div><div><h2><a href=../../../../publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</a></h2><div class=article-style>Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. Graph Convolutional Networks (GCN) allow this projection, but existing explainability …</div></div><div><h2><a href=../../../../publication/acl2019-alt-finetuning/>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</a></h2><div class=article-style>We show that generative language model pre-training combined with selective attention improves recall for long-tail relations in distantly supervised neural relation extraction.</div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../../../publication-type/1/page/3/>&#171;</a></li><li class=page-item><a class=page-link href=../../../../publication-type/1/page/5/>&#187;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../../../privacy/>Data Protection Notice</a>
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diff --git a/publication-type/1/page/5/index.html b/publication-type/1/page/5/index.html
index 0433f8e1..3ee33763 100644
--- a/publication-type/1/page/5/index.html
+++ b/publication-type/1/page/5/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>1</h1></div><div class=universal-wrapper><div><h2><a href=../../../../publication/qomex2019-iskender-crowd/>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</a></h2><div class=article-style>We analyze the feasibility and appropriateness of micro-task crowdsourcing for evaluation of different summary quality characteristics and report an ongoing work on the crowdsourced evaluation of query-based extractive text summaries</div></div><div><h2><a href=../../../../publication/ostendorff2019/>Enriching BERT with Knowledge Graph Embedding for Document Classification</a></h2><div class=article-style>In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and …</div></div><div><h2><a href=../../../../publication/akbc2019-alt-improving/>Improving Relation Extraction by Pre-Trained Language Representations</a></h2><div class=article-style>We show that transfer learning through generative language model pre-training improves supervised neural relation extraction, achieving new state-of-the-art performance on TACRED and SemEval 2010 Task 8.</div></div><div><h2><a href=../../../../publication/nvc/>Neural Vector Conceptualization for Word Vector Space Interpretation</a></h2><div class=article-style>Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, …</div></div><div><h2><a href=../../../../publication/diamat/>Train, Sort, Explain: Learning to Diagnose Translation Models</a></h2><div class=article-style>Evaluating translation models is a trade-off between effort and detail. On the one end of the spectrum there are automatic count-based methods such as BLEU, on the other end linguistic evaluations by humans, which arguably are more informative but …</div></div><div><h2><a href=../../../../publication/mex-an-information-extraction-platform-for-german-medical-text/>mEx - an Information Extraction Platform for German Medical Text</a></h2><div class=article-style></div></div><div><h2><a href=../../../../publication/learning-explanations-from-language-data/>Learning Explanations From Language Data</a></h2><div class=article-style>PatternAttribution is a recent method, introduced in the vision domain, that explains classifications of deep neural networks. We demonstrate that it also generates meaningful interpretations in the language domain.</div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../../../publication-type/1/page/4/>&#171;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../../../privacy/>Data Protection Notice</a>
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diff --git a/publication-type/2/index.html b/publication-type/2/index.html
index 60d07929..799ef20b 100644
--- a/publication-type/2/index.html
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@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>2</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/shti2023-abdulnazar-sapbert/>SapBERT-Based Medical Concept Normalization Using SNOMED CT</a></h2><div class=article-style>Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized representations have been very successful recently. In this work, we analyze the impact of contextualized and …</div></div><div><h2><a href=../../publication/plosone2023-roller-performance/>When performance is not enough—A multidisciplinary view on clinical decision support</a></h2><div class=article-style>Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make …</div></div><div><h2><a href=../../publication/database2023-roller-biocreative/>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</a></h2><div class=article-style>This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user’s publicly available tweets (the user’s ‘timeline’). In general, detecting health-related …</div></div><div><h2><a href=../../publication/frontiers2022-roller-nothing/>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</a></h2><div class=article-style>Introduction: Artificial intelligence–driven decision support systems (AI–DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often …</div></div><div><h2><a href=../../publication/frontiers2022-roller-evaluation/>Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation</a></h2><div class=article-style>Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication-type/2/index.xml b/publication-type/2/index.xml
index 1b0cbead..aff6c5d4 100644
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diff --git a/publication-type/5/index.html b/publication-type/5/index.html
index b401f7d3..7504719b 100644
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>5</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/samhammer-2023-klinische/>Klinische Entscheidungsfindung mit Künstlicher Intelligenz: Ein interdisziplinärer Governance-Ansatz</a></h2><div class=article-style>Dieses Open-Access-essential schafft Orientierung, wenn Künstliche Intelligenz im klinischen Alltag eingesetzt wird. Die Herausforderungen werden anhand zweier Beispiele aus dem Bereich der Nephrologie erläutert, die ethisch und rechtlich reflektiert …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication-type/5/index.xml b/publication-type/5/index.xml
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diff --git a/publication-type/6/index.html b/publication-type/6/index.html
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+++ b/publication-type/6/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>6</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/vandeghinste-2023-ele-sign/>European Language Equality, Report on Europe's Sign Languages</a></h2><div class=article-style>This report on Europe's Sign Languages is part of a series of language deliverables developed within the framework of the European Language Equality (ELE) project. The series seeks to not only delineate the current state of affairs for each European …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication-type/6/index.xml b/publication-type/6/index.xml
index b6fa58e9..e9440a57 100644
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>6 | DFKI-NLP</title><link>https://dfki-nlp.github.io/publication-type/6/</link><atom:link href="https://dfki-nlp.github.io/publication-type/6/index.xml" rel="self" type="application/rss+xml"/><description>6</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 21 Jul 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>6</title><link>https://dfki-nlp.github.io/publication-type/6/</link></image><item><title>European Language Equality, Report on Europe's Sign Languages</title><link>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</link><pubDate>Fri, 21 Jul 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</guid><description/></item></channel></rss>
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diff --git a/publication/acl-ecnlp2020-zhang-bootstrapping/index.html b/publication/acl-ecnlp2020-zhang-bootstrapping/index.html
index 069d4385..c03897a7 100644
--- a/publication/acl-ecnlp2020-zhang-bootstrapping/index.html
+++ b/publication/acl-ecnlp2020-zhang-bootstrapping/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/plass/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.18653/v1/2020.ecnlp-1.1 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>In this work, we introduce a bootstrapped, iterative NER model that integrates a PU learning algorithm for recognizing named entities in a low-resource setting. Our approach combines dictionary-based labeling with syntactically-informed label expansion to efficiently enrich the seed dictionaries. Experimental results on a dataset of manually annotated e-commerce product descriptions demonstrate the effectiveness of the proposed framework.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of The 3rd Workshop on e-Commerce and NLP, Association for Computational Linguistics</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/acl-srw2020-harbecke-considering/index.html b/publication/acl-srw2020-harbecke-considering/index.html
index 327516f5..14ae1ff1 100644
--- a/publication/acl-srw2020-harbecke-considering/index.html
+++ b/publication/acl-srw2020-harbecke-considering/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/bbdc2/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.18653/v1/2020.acl-srw.16 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occlusion is a well established method that provides explanations on discrete language data, e.g. by removing a language unit from an input and measuring the impact on a model&rsquo;s decision. We argue that current occlusion-based methods often produce invalid or syntactically incorrect language data, neglecting the improved abilities of recent NLP models. Furthermore, gradient-based explanation methods disregard the discrete distribution of data in NLP. Thus, we propose OLM: a novel explanation method that combines occlusion and language models to sample valid and syntactically correct replacements with high likelihood, given the context of the original input. We lay out a theoretical foundation that alleviates these weaknesses of other explanation methods in NLP and provide results that underline the importance of considering data likelihood in occlusion-based explanation.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/explainability/>Explainability</a></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../publication/emnlp2023-feldhus-interrolang/>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/publication/acl2019-alt-finetuning/index.html b/publication/acl2019-alt-finetuning/index.html
index 4d7f26bd..e597a883 100644
--- a/publication/acl2019-alt-finetuning/index.html
+++ b/publication/acl2019-alt-finetuning/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/TRE target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../publication/acl2019-alt-finetuning/alt-etal-2019-fine-poster.pdf target=_blank rel=noopener>Poster</a></div></div><div class=article-container><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/acl2020-alt-probing/index.html b/publication/acl2020-alt-probing/index.html
index 5e36755c..82faab20 100644
--- a/publication/acl2020-alt-probing/index.html
+++ b/publication/acl2020-alt-probing/index.html
@@ -9,7 +9,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/plass/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.18653/v1/2020.acl-main.140 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, conditioned on the entity mentions, before classifying the relation. However, the complexity of the task makes it difficult to understand how encoder architecture and supporting linguistic knowledge affect the features learned by the encoder. We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance. For example, adding contextualized word representations greatly increases performance on probing tasks with a focus on named entity and part-of-speech information, and yields better results in RE. In contrast, entity masking improves RE, but considerably lowers performance on entity type related probing tasks.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/aleksandra-gabryszak/><img class="avatar mr-3 avatar-circle" src=../../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Aleksandra Gabryszak"></a><div class=media-body><h5 class=card-title><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/acl2020-alt-tacred-revisited/index.html b/publication/acl2020-alt-tacred-revisited/index.html
index e337f6e1..98cca5e7 100644
--- a/publication/acl2020-alt-tacred-revisited/index.html
+++ b/publication/acl2020-alt-tacred-revisited/index.html
@@ -9,7 +9,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/plass/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.18653/v1/2020.acl-main.142 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>TACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training and knowledge enhanced neural RE, models still show a high error rate. In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement? And how do crowd annotations, dataset, and models contribute to this error rate? To answer these questions, we first validate the most challenging 5K examples in the development and test sets using trained annotators. We find that label errors account for 8% absolute F1 test error, and that more than 50% of the examples need to be relabeled. On the relabeled test set the average F1 score of a large baseline model set improves from 62.1 to 70.1. After validation, we analyze misclassifications on the challenging instances, categorize them into linguistically motivated error groups, and verify the resulting error hypotheses on three state-of-the-art RE models. We show that two groups of ambiguous relations are responsible for most of the remaining errors and that models may adopt shallow heuristics on the dataset when entities are not masked.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/aleksandra-gabryszak/><img class="avatar mr-3 avatar-circle" src=../../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Aleksandra Gabryszak"></a><div class=media-body><h5 class=card-title><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/acl2022-humeval-macketanz-perceptual/index.html b/publication/acl2022-humeval-macketanz-perceptual/index.html
index ba9c7bed..f80198e2 100644
--- a/publication/acl2022-humeval-macketanz-perceptual/index.html
+++ b/publication/acl2022-humeval-macketanz-perceptual/index.html
@@ -6,7 +6,7 @@
 <a class="badge badge-light" href=../../tag/semantic-differential/>semantic differential</a>
 <a class="badge badge-light" href=../../tag/machine-generated-text/>machine-generated text</a></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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diff --git a/publication/acl2022-nlppower-harbecke-f1/index.html b/publication/acl2022-nlppower-harbecke-f1/index.html
index 75ae0ff0..328ab7be 100644
--- a/publication/acl2022-nlppower-harbecke-f1/index.html
+++ b/publication/acl2022-nlppower-harbecke-f1/index.html
@@ -5,7 +5,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/cora4nlp/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/plass/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce a framework for weighting schemes, where existing schemes are extremes, and two new intermediate schemes. We show that reporting results of different weighting schemes better highlights strengths and weaknesses of a model.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 1st Workshop on Efficient Benchmarking in NLP</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/yuxuan-chen/><img class="avatar mr-3 avatar-circle" src=../../authors/yuxuan-chen/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Yuxuan Chen"></a><div class=media-body><h5 class=card-title><a href=../../authors/yuxuan-chen/>Yuxuan Chen</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:yuxuan.chen@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/chen-yuxuan target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/acl2022-repl4nlp-chen-fewie/index.html b/publication/acl2022-repl4nlp-chen-fewie/index.html
index b3d4f6b4..afdff11c 100644
--- a/publication/acl2022-repl4nlp-chen-fewie/index.html
+++ b/publication/acl2022-repl4nlp-chen-fewie/index.html
@@ -7,7 +7,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/cora4nlp/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/plass/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data. However, their performance in low-resource scenarios, where such data is not available, remains an open question. We introduce an encoder evaluation framework, and use it to systematically compare the performance of state-of-the-art pre-trained representations on the task of low-resource NER. We analyze a wide range of encoders pre-trained with different strategies, model architectures, intermediate-task fine-tuning, and contrastive learning. Our experimental results across ten benchmark NER datasets in English and German show that encoder performance varies significantly, suggesting that the choice of encoder for a specific low-resource scenario needs to be carefully evaluated.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 7th Workshop on Representation Learning for NLP</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/yuxuan-chen/><img class="avatar mr-3 avatar-circle" src=../../authors/yuxuan-chen/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Yuxuan Chen"></a><div class=media-body><h5 class=card-title><a href=../../authors/yuxuan-chen/>Yuxuan Chen</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:yuxuan.chen@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/chen-yuxuan target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/arne-binder/><img class="avatar mr-3 avatar-circle" src=../../authors/arne-binder/avatar_huf1b341a11428bbdba6a06391dba9a6b4_16118_270x270_fill_q90_lanczos_center.jpg alt="Arne Binder"></a><div class=media-body><h5 class=card-title><a href=../../authors/arne-binder/>Arne Binder</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:arne.binder@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/ArneBinder target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/acl2022-ruan/index.html b/publication/acl2022-ruan/index.html
index 79283040..20c46ff2 100644
--- a/publication/acl2022-ruan/index.html
+++ b/publication/acl2022-ruan/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/QianRuan/histruct target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.18653/v1/2022.findings-acl.102 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We propose a novel approach to formulate, extract, encode and inject hierarchical structure information explicitly into an extractive summarization model based on a pre-trained, encoder-only Transformer language model (HiStruct+ model), which improves SOTA ROUGEs for extractive summarization on PubMed and arXiv substantially. Using various experimental settings on three datasets (i.e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected. It is also observed that the more conspicuous hierarchical structure the dataset has, the larger improvements our method gains. The ablation study demonstrates that the hierarchical position information is the main contributor to our model’s SOTA performance.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Findings of the Association for Computational Linguistics: ACL 2022</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/acl2023-feldhus-smv/index.html b/publication/acl2023-feldhus-smv/index.html
index cdf17c4d..8caf40d8 100644
--- a/publication/acl2023-feldhus-smv/index.html
+++ b/publication/acl2023-feldhus-smv/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/acl2023-feldhus-smv/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/SMV/ target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Saliency maps can explain a neural model&rsquo;s predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more accessible, we formalize the underexplored task of translating saliency maps into natural language and compare methods that address two key challenges of this approach &ndash; what and how to verbalize. In both automatic and human evaluation setups, using token-level attributions from text classification tasks, we compare two novel methods (search-based and instruction-based verbalizations) against conventional feature importance representations (heatmap visualizations and extractive rationales), measuring simulatability, faithfulness, helpfulness and ease of understanding. Instructing GPT-3.5 to generate saliency map verbalizations yields plausible explanations which include associations, abstractive summarization and commonsense reasoning, achieving by far the highest human ratings, but they are not faithfully capturing numeric information and are inconsistent in their interpretation of the task. In comparison, our search-based, model-free verbalization approach efficiently completes templated verbalizations, is faithful by design, but falls short in helpfulness and simulatability. Our results suggest that saliency map verbalization makes feature attribution explanations more comprehensible and less cognitively challenging to humans than conventional representations.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the First Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/interpretability/>Interpretability</a></div><div class="media author-card content-widget-hr"><a href=../../authors/nils-feldhus/><img class="avatar mr-3 avatar-circle" src=../../authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt="Nils Feldhus"></a><div class=media-body><h5 class=card-title><a href=../../authors/nils-feldhus/>Nils Feldhus</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../publication/acl2023-sarti-inseq/>Inseq: An Interpretability Toolkit for Sequence Generation Models</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/acl2023-hennig-multitacred/index.html b/publication/acl2023-hennig-multitacred/index.html
index f7901a36..7146277a 100644
--- a/publication/acl2023-hennig-multitacred/index.html
+++ b/publication/acl2023-hennig-multitacred/index.html
@@ -9,7 +9,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/text2tech/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.48550/arXiv.2305.04582 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. We analyze translation and annotation projection quality, identify error categories, and experimentally evaluate fine-tuned pretrained mono- and multilingual language models in common transfer learning scenarios. Our analyses show that machine translation is a viable strategy to transfer RE instances, with native speakers judging more than 83% of the translated instances to be linguistically and semantically acceptable. We find monolingual RE model performance to be comparable to the English original for many of the target languages, and that multilingual models trained on a combination of English and target language data can outperform their monolingual counterparts. However, we also observe a variety of translation and annotation projection errors, both due to the MT systems and linguistic features of the target languages, such as pronoun-dropping, compounding and inflection, that degrade dataset quality and RE model performance.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/philippe-thomas/><img class="avatar mr-3 avatar-circle" src=../../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt="Philippe Thomas"></a><div class=media-body><h5 class=card-title><a href=../../authors/philippe-thomas/>Philippe Thomas</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/acl2023-rethmeier-vendorlink/index.html b/publication/acl2023-rethmeier-vendorlink/index.html
index bf79fbf7..e93673b2 100644
--- a/publication/acl2023-rethmeier-vendorlink/index.html
+++ b/publication/acl2023-rethmeier-vendorlink/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/maastrichtlawtech/VendorLink target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/cora4nlp/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. Consequently, illegal markets and their connections are challenging to uncover on the Darknet. To identify relationships between illegal markets and their vendors, we propose VendorLink, an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements (ads) on seven public Darknet markets. In contrast to existing literature, VendorLink utilizes the strength of supervised pretraining to perform closed-set vendor verification, open-set vendor identification, and low-resource market adaption tasks. Through VendorLink, we uncover (i) 15 migrants and 71 potential aliases in the Alphabay-Dreams-Silk dataset, (ii) 17 migrants and 3 potential aliases in the Valhalla-Berlusconi dataset, and (iii) 75 migrants and 10 potential aliases in the Traderoute-Agora dataset. Altogether, our approach can help Law Enforcement Agencies (LEA) make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/nils-rethmeier/><img class="avatar mr-3 avatar-circle" src=../../authors/nils-rethmeier/avatar_hu06230aa9f5e4bab2d279f74300374684_30872_270x270_fill_q90_lanczos_center.jpg alt="Nils Rethmeier"></a><div class=media-body><h5 class=card-title><a href=../../authors/nils-rethmeier/>Nils Rethmeier</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.rethmeier@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/NilsRethmeier target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/acl2023-sarti-inseq/index.html b/publication/acl2023-sarti-inseq/index.html
index 1f6f0e44..646008d1 100644
--- a/publication/acl2023-sarti-inseq/index.html
+++ b/publication/acl2023-sarti-inseq/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/acl2023-sarti-inseq/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/inseq-team/inseq target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools. In this work, we introduce Inseq, a Python library to democratize access to interpretability analyses of sequence generation models. Inseq enables intuitive and optimized extraction of models' internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. We showcase its potential by adopting it to highlight gender biases in machine translation models and locate factual knowledge inside GPT-2. Thanks to its extensible interface supporting cutting-edge techniques such as contrastive feature attribution, Inseq can drive future advances in explainable natural language generation, centralizing good practices and enabling fair and reproducible model evaluations.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: System Demonstrations</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/interpretability/>Interpretability</a></div><div class="media author-card content-widget-hr"><a href=../../authors/nils-feldhus/><img class="avatar mr-3 avatar-circle" src=../../authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt="Nils Feldhus"></a><div class=media-body><h5 class=card-title><a href=../../authors/nils-feldhus/>Nils Feldhus</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../publication/acl2023-feldhus-smv/>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/acl2023-zhu-sign/index.html b/publication/acl2023-zhu-sign/index.html
index e8ac6714..9935fc67 100644
--- a/publication/acl2023-zhu-sign/index.html
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@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</h1><div class=article-metadata><div><span><a href=../../authors/dele-zhu/>Dele Zhu</a></span>, <span><a href=../../authors/vera-czehmann/>Vera Czehmann</a></span>, <span><a href=../../authors/eleftherios-avramidis/>Eleftherios Avramidis</a></span></div><span class=article-date>June 2023</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://aclanthology.org/2023.acl-long.700.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/acl2023-zhu-sign/cite.bib>Cite</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language (SL) glosses. In our experiments, we improve the performance of the transformer-based models via (1) data augmentation, (2) semi-supervised Neural Machine Translation (NMT), (3) transfer learning and (4) multilingual NMT. The proposed methods are implemented progressively on two German SL corpora containing gloss annotations. Multilingual NMT combined with data augmentation appear to be the most successful setting, yielding statistically significant improvements as measured by three automatic metrics (up to over 6 points BLEU), and confirmed via human evaluation. Our best setting outperforms all previous work that report on the same test-set and is also confirmed on a corpus of the American Sign Language (ASL).</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/machine-translation/>Machine Translation</a></div><div class="media author-card content-widget-hr"><a href=../../authors/eleftherios-avramidis/><img class="avatar mr-3 avatar-circle" src=../../authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt="Eleftherios Avramidis"></a><div class=media-body><h5 class=card-title><a href=../../authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../publication/vandeghinste-2023-ele-sign/>European Language Equality, Report on Europe's Sign Languages</a></li><li><a href=../../publication/lrec2022-macketanz-testsuite/>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/akbc2019-alt-improving/index.html b/publication/akbc2019-alt-improving/index.html
index 7259c9ee..05a0ef8b 100644
--- a/publication/akbc2019-alt-improving/index.html
+++ b/publication/akbc2019-alt-improving/index.html
@@ -7,7 +7,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../publication/akbc2019-alt-improving/alt-etal-2019-improving-poster.pdf target=_blank rel=noopener>Poster</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../publication/akbc2019-alt-improving/alt-etal-2019-improving-slides.pdf target=_blank rel=noopener>Slides</a></div></div><div class=article-container><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of Automated Knowledge Base Construction 2019</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/cl-nvc/index.html b/publication/cl-nvc/index.html
index 82e1ae06..d80e9fe5 100644
--- a/publication/cl-nvc/index.html
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@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/cl-nvc/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/cross-nvc target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Recently, Neural Vector Conceptualization (NVC) was proposed as a means to interpret samples from a word vector space. For NVC, a neural model activates higher order concepts it recognizes in a word vector instance. To this end, the model first needs to be trained with a sufficiently large instance-to-concept ground truth, which only exists for a few languages. In this work, we tackle this lack of resources with word vector space alignment techniques: We train the NVC model on a high resource language and test it with vectors from an aligned word vector space of another language, without retraining or fine-tuning. A quantitative and qualitative analysis shows that the NVC model indeed activates meaningful concepts for unseen vectors from the aligned vector space. NVC thus becomes available for low resource languages for which no appropriate concept ground truth exists.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">NLPCC 2019 - Springer Lecture Notes in Computer Science</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/lisa-raithel/><img class="avatar mr-3 avatar-circle" src=../../authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt="Lisa Raithel"></a><div class=media-body><h5 class=card-title><a href=../../authors/lisa-raithel/>Lisa Raithel</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.linkedin.com/in/lisa-raithel-3b2865240/ target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/database2023-roller-biocreative/index.html b/publication/database2023-roller-biocreative/index.html
index 80c231d7..f258143c 100644
--- a/publication/database2023-roller-biocreative/index.html
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@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/database2023-roller-biocreative/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.1093/database/baac108 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user’s publicly available tweets (the user’s ‘timeline’). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user’s timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user’s timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at <a href=https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/>https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/</a>. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#2>Journal article</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Database</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/diamat/index.html b/publication/diamat/index.html
index 41f3fff7..de5f857b 100644
--- a/publication/diamat/index.html
+++ b/publication/diamat/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/diamat/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/dfki-nlp/lrv target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Evaluating translation models is a trade-off between effort and detail. On the one end of the spectrum there are automatic count-based methods such as BLEU, on the other end linguistic evaluations by humans, which arguably are more informative but also require a disproportionately high effort. To narrow the spectrum, we propose a general approach on how to automatically expose systematic differences between human and machine translations to human experts. Inspired by adversarial settings, we train a neural text classifier to distinguish human from machine translations. A classifier that performs and generalizes well after training should recognize systematic differences between the two classes, which we uncover with neural explainability methods. Our proof-of-concept implementation, DiaMaT, is open source. Applied to a dataset translated by a state-of-the-art neural Transformer model, DiaMaT achieves a classification accuracy of 75% and exposes meaningful differences between humans and the Transformer, amidst the current discussion about human parity.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/eleftherios-avramidis/><img class="avatar mr-3 avatar-circle" src=../../authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt="Eleftherios Avramidis"></a><div class=media-body><h5 class=card-title><a href=../../authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/emnlp2022-chen-meffiprompt/index.html b/publication/emnlp2022-chen-meffiprompt/index.html
index dbb73ed4..46490805 100644
--- a/publication/emnlp2022-chen-meffiprompt/index.html
+++ b/publication/emnlp2022-chen-meffiprompt/index.html
@@ -7,7 +7,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/cora4nlp/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.48550/arXiv.2210.13838 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual settings, applying prompt-based methods in multilingual scenarios has been limited to a narrow set of tasks, due to the high cost of handcrafting multilingual prompts. In this paper, we present the first work on prompt-based multilingual relation classification (RC), by introducing an efficient and effective method that constructs prompts from relation triples and involves only minimal translation for the class labels. We evaluate its performance in fully supervised, few-shot and zero-shot scenarios, and analyze its effectiveness across 14 languages, prompt variants, and English-task training in cross-lingual settings. We find that in both fully supervised and few-shot scenarios, our prompt method beats competitive baselines: fine-tuning XLM-R-EM and null prompts. It also outperforms the random baseline by a large margin in zero-shot experiments. Our method requires little in-language knowledge and can be used as a strong baseline for similar multilingual classification tasks.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/yuxuan-chen/><img class="avatar mr-3 avatar-circle" src=../../authors/yuxuan-chen/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Yuxuan Chen"></a><div class=media-body><h5 class=card-title><a href=../../authors/yuxuan-chen/>Yuxuan Chen</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:yuxuan.chen@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/chen-yuxuan target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/emnlp2022-ostendorff/index.html b/publication/emnlp2022-ostendorff/index.html
index cfaedf00..ee361f50 100644
--- a/publication/emnlp2022-ostendorff/index.html
+++ b/publication/emnlp2022-ostendorff/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/malteos/scincl target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.48550/arXiv.2202.06671 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics. Prior work relies on discrete citation relations to generate contrast samples. However, discrete citations enforce a hard cut-off to similarity. This is counter-intuitive to similarity-based learning, and ignores that scientific papers can be very similar despite lacking a direct citation - a core problem of finding related research. Instead, we use controlled nearest neighbor sampling over citation graph embeddings for contrastive learning. This control allows us to learn continuous similarity, to sample hard-to-learn negatives and positives, and also to avoid collisions between negative and positive samples by controlling the sampling margin between them. The resulting method SciNCL outperforms the state-of-the-art on the SciDocs benchmark. Furthermore, we demonstrate that it can train (or tune) models sample-efficiently, and that it can be combined with recent training-efficient methods. Perhaps surprisingly, even training a general-domain language model this way outperforms baselines pretrained in-domain.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/nils-rethmeier/><img class="avatar mr-3 avatar-circle" src=../../authors/nils-rethmeier/avatar_hu06230aa9f5e4bab2d279f74300374684_30872_270x270_fill_q90_lanczos_center.jpg alt="Nils Rethmeier"></a><div class=media-body><h5 class=card-title><a href=../../authors/nils-rethmeier/>Nils Rethmeier</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.rethmeier@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/NilsRethmeier target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/emnlp2023-feldhus-interrolang/index.html b/publication/emnlp2023-feldhus-interrolang/index.html
index bfbaa05f..515b0657 100644
--- a/publication/emnlp2023-feldhus-interrolang/index.html
+++ b/publication/emnlp2023-feldhus-interrolang/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/InterroLang target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.48550/arXiv.2310.05592 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such a dialogue system can help users explore datasets and models with explanations in a contextualized manner, e.g. via clarification or follow-up questions, and through a natural language interface. We adapt the conversational explanation framework TalkToModel (Slack et al., 2022) to the NLP domain, add new NLP-specific operations such as free-text rationalization, and illustrate its generalizability on three NLP tasks (dialogue act classification, question answering, hate speech detection). To recognize user queries for explanations, we evaluate fine-tuned and few-shot prompting models and implement a novel adapter-based approach. We then conduct two user studies on (1) the perceived correctness and helpfulness of the dialogues, and (2) the simulatability, i.e. how objectively helpful dialogical explanations are for humans in figuring out the model&rsquo;s predicted label when it&rsquo;s not shown. We found rationalization and feature attribution were helpful in explaining the model behavior. Moreover, users could more reliably predict the model outcome based on an explanation dialogue rather than one-off explanations.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Findings of the Association for Computational Linguistics: EMNLP 2023</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/explainability/>Explainability</a></div><div class="media author-card content-widget-hr"><a href=../../authors/nils-feldhus/><img class="avatar mr-3 avatar-circle" src=../../authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt="Nils Feldhus"></a><div class=media-body><h5 class=card-title><a href=../../authors/nils-feldhus/>Nils Feldhus</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../publication/acl-srw2020-harbecke-considering/>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/frontiers2022-roller-evaluation/index.html b/publication/frontiers2022-roller-evaluation/index.html
index 40e51180..ba4e5b91 100644
--- a/publication/frontiers2022-roller-evaluation/index.html
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@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/frontiers2022-roller-evaluation/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.3389/fpubh.2022.979448 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, performance metrics alone do not determine practical utility. We present a newly developed clinical decision support system (CDSS) for detection of patients at risk for rejection and death-censored graft failure. The CDSS is based on clinical routine data including 1,516 kidney transplant recipients and more than 100,000 data points. In a reader study we compare the performance of physicians at a nephrology department with and without the CDSS. Internal validation shows AUC-ROC scores of 0.83 for rejection, and 0.95 for graft failure. The reader study shows that predictions by physicians converge toward the CDSS. However, performance does not improve (AUC–ROC; 0.6413 vs. 0.6314 for rejection; 0.8072 vs. 0.7778 for graft failure). Finally, the study shows that the CDSS detects partially different patients at risk compared to physicians. This indicates that the combination of both, medical professionals and a CDSS might help detect more patients at risk for graft failure. However, the question of how to integrate such a system efficiently into clinical practice remains open.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#2>Journal article</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Frontiers in Public Health</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/frontiers2022-roller-nothing/index.html b/publication/frontiers2022-roller-nothing/index.html
index 010bdd83..6138ac41 100644
--- a/publication/frontiers2022-roller-nothing/index.html
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@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/frontiers2022-roller-nothing/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.3389/fmed.2022.1016366 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Introduction: Artificial intelligence–driven decision support systems (AI–DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention. This article aims to shed light on the needs and challenges arising from the use of AI-DSS from physicians' perspectives. Methods: The basis for this study is a qualitative content analysis of expert interviews with experienced nephrologists after testing an AI-DSS in a straightforward usage scenario. Results: The results provide insights on the basics of clinical decision-making, expected challenges when using AI-DSS as well as a reflection on the test run. Discussion: While we can confirm the somewhat expectable demand for better explainability and control, other insights highlight the need to uphold classical strengths of the medical profession when using AI-DSS as well as the importance of broadening the view of AI-related challenges to the clinical environment, especially during treatment. Our results stress the necessity for adjusting AI-DSS to shared decision-making. We conclude that explainability must be context-specific while fostering meaningful interaction with the systems available.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#2>Journal article</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Frontiers in Medicine</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/icml-schwarzenberg-castle/index.html b/publication/icml-schwarzenberg-castle/index.html
index 7ad3abdf..b48076ab 100644
--- a/publication/icml-schwarzenberg-castle/index.html
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@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/icml-schwarzenberg-castle/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/pgig target=_blank rel=noopener>Code</a></div></div><div class=article-container><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">ICML 2020 Workshop on Human Interpretability in Machine Learning</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/steffen-castle/><img class="avatar mr-3 avatar-circle" src=../../authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Steffen Castle"></a><div class=media-body><h5 class=card-title><a href=../../authors/steffen-castle/>Steffen Castle</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/index.html b/publication/index.html
index be6f087b..dd4f7830 100644
--- a/publication/index.html
+++ b/publication/index.html
@@ -304,7 +304,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal" data-filename=/publication/learning-explanations-from-language-data/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header btn-sm" href=https://github.com/DFKI-NLP/language-attributions target=_blank rel=noopener>Code</a></p></div></div></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
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diff --git a/publication/index.xml b/publication/index.xml
index c82e57d7..d72b2c7b 100644
--- a/publication/index.xml
+++ b/publication/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Publications | DFKI-NLP</title><link>https://dfki-nlp.github.io/publication/</link><atom:link href="https://dfki-nlp.github.io/publication/index.xml" rel="self" type="application/rss+xml"/><description>Publications</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 06 Dec 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Publications</title><link>https://dfki-nlp.github.io/publication/</link></image><item><title>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</title><link>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</link><pubDate>Wed, 06 Dec 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</guid><description/></item><item><title>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</title><link>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</link><pubDate>Thu, 17 Aug 2023 08:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2023-binsumait-etal-factuality/</guid><description/></item><item><title>European Language Equality, Report on Europe's Sign Languages</title><link>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</link><pubDate>Fri, 21 Jul 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</guid><description/></item><item><title>Inseq: An Interpretability Toolkit for Sequence Generation Models</title><link>https://dfki-nlp.github.io/publication/acl2023-sarti-inseq/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-sarti-inseq/</guid><description/></item><item><title>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</title><link>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</guid><description/></item><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning</title><link>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</link><pubDate>Tue, 16 May 2023 09:39:26 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</guid><description/></item><item><title>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</title><link>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</guid><description/></item><item><title>SapBERT-Based Medical Concept Normalization Using SNOMED CT</title><link>https://dfki-nlp.github.io/publication/shti2023-abdulnazar-sapbert/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/shti2023-abdulnazar-sapbert/</guid><description/></item><item><title>VendorLink: An NLP approach for Identifying &amp; Linking Vendor Migrants &amp; Potential Aliases on Darknet Markets</title><link>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</guid><description/></item><item><title>Klinische Entscheidungsfindung mit Künstlicher Intelligenz: Ein interdisziplinärer Governance-Ansatz</title><link>https://dfki-nlp.github.io/publication/samhammer-2023-klinische/</link><pubDate>Tue, 02 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/samhammer-2023-klinische/</guid><description/></item><item><title>When performance is not enough—A multidisciplinary view on clinical decision support</title><link>https://dfki-nlp.github.io/publication/plosone2023-roller-performance/</link><pubDate>Sun, 23 Apr 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/plosone2023-roller-performance/</guid><description/></item><item><title>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</title><link>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</link><pubDate>Wed, 15 Feb 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</guid><description/></item><item><title>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</title><link>https://dfki-nlp.github.io/publication/frontiers2022-roller-nothing/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/frontiers2022-roller-nothing/</guid><description/></item><item><title>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</title><link>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</guid><description/></item><item><title>Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation</title><link>https://dfki-nlp.github.io/publication/frontiers2022-roller-evaluation/</link><pubDate>Tue, 25 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/frontiers2022-roller-evaluation/</guid><description/></item><item><title>Multilingual Relation Classification via Efficient and Effective Prompting</title><link>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</link><pubDate>Sat, 22 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</guid><description/></item><item><title>Full-Text Argumentation Mining on Scientific Publications</title><link>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</link><pubDate>Fri, 21 Oct 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</guid><description/></item><item><title>Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings</title><link>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</link><pubDate>Fri, 14 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</guid><description/></item><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item><item><title>An Annotated Corpus of Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/publication/lrec2022-roller/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-roller/</guid><description/></item><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item><item><title>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</guid><description/></item><item><title>Subjective Text Complexity Assessment for German</title><link>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</guid><description/></item><item><title>Claim Extraction and Law Matching for COVID-19-related Legislation</title><link>https://dfki-nlp.github.io/publication/lrec2022-dehio/</link><pubDate>Wed, 06 Jul 2022 09:37:49 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-dehio/</guid><description/></item><item><title>Specialized Document Embeddings for Aspect-based Similarity of Research Papers</title><link>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</link><pubDate>Wed, 06 Jul 2022 09:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</guid><description/></item><item><title>HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information</title><link>https://dfki-nlp.github.io/publication/acl2022-ruan/</link><pubDate>Wed, 06 Jul 2022 09:25:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-ruan/</guid><description/></item><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item><item><title>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</title><link>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</guid><description/></item><item><title>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</title><link>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</guid><description/></item><item><title>Detecting Covariate Drift with Explanations</title><link>https://dfki-nlp.github.io/publication/nlpcc-castle/</link><pubDate>Fri, 15 Oct 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/nlpcc-castle/</guid><description/></item><item><title>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</link><pubDate>Tue, 07 Sep 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</guid><description/></item><item><title>Evaluating Document Representations for Content-based Legal Literature Recommendations</title><link>https://dfki-nlp.github.io/publication/ostendorff2021/</link><pubDate>Thu, 27 May 2021 12:18:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2021/</guid><description/></item><item><title>Aspect-based Document Similarity for Research Papers</title><link>https://dfki-nlp.github.io/publication/ostendorff2020c/</link><pubDate>Sun, 27 Dec 2020 12:17:32 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020c/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles</title><link>https://dfki-nlp.github.io/publication/ostendorff2020/</link><pubDate>Thu, 27 Aug 2020 12:17:42 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020/</guid><description/></item><item><title>Pattern-Guided Integrated Gradients</title><link>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</link><pubDate>Wed, 29 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</guid><description/></item><item><title>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</title><link>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</guid><description/></item><item><title>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</title><link>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</guid><description/></item><item><title>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</guid><description/></item><item><title>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</guid><description/></item><item><title>Evaluating German Transformer Language Models with Syntactic Agreement Tests</title><link>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</link><pubDate>Tue, 23 Jun 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Cross-lingual Neural Vector Conceptualization</title><link>https://dfki-nlp.github.io/publication/cl-nvc/</link><pubDate>Mon, 30 Sep 2019 18:09:58 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/cl-nvc/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</link><pubDate>Mon, 29 Jul 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</guid><description/></item><item><title>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</title><link>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</link><pubDate>Mon, 24 Jun 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</guid><description/></item><item><title>Enriching BERT with Knowledge Graph Embedding for Document Classification</title><link>https://dfki-nlp.github.io/publication/ostendorff2019/</link><pubDate>Mon, 27 May 2019 12:32:18 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2019/</guid><description/></item><item><title>Improving Relation Extraction by Pre-Trained Language Representations</title><link>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</link><pubDate>Mon, 20 May 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</guid><description/></item><item><title>Neural Vector Conceptualization for Word Vector Space Interpretation</title><link>https://dfki-nlp.github.io/publication/nvc/</link><pubDate>Tue, 02 Apr 2019 17:55:21 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/nvc/</guid><description/></item><item><title>Train, Sort, Explain: Learning to Diagnose Translation Models</title><link>https://dfki-nlp.github.io/publication/diamat/</link><pubDate>Thu, 28 Mar 2019 17:40:45 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/diamat/</guid><description/></item><item><title>mEx - an Information Extraction Platform for German Medical Text</title><link>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</link><pubDate>Sat, 01 Dec 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</guid><description/></item><item><title>Learning Explanations From Language Data</title><link>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</link><pubDate>Fri, 02 Nov 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</guid><description/></item></channel></rss>
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+0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-sarti-inseq/</guid><description/></item><item><title>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</title><link>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</guid><description/></item><item><title>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</title><link>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-feldhus-smv/</guid><description/></item><item><title>Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning</title><link>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</link><pubDate>Tue, 16 May 2023 09:39:26 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/pml4dc-2023-ostendorff/</guid><description/></item><item><title>MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset</title><link>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-hennig-multitacred/</guid><description/></item><item><title>SapBERT-Based Medical Concept Normalization Using SNOMED CT</title><link>https://dfki-nlp.github.io/publication/shti2023-abdulnazar-sapbert/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/shti2023-abdulnazar-sapbert/</guid><description/></item><item><title>VendorLink: An NLP approach for Identifying &amp; Linking Vendor Migrants &amp; Potential Aliases on Darknet Markets</title><link>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</link><pubDate>Mon, 08 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-rethmeier-vendorlink/</guid><description/></item><item><title>Klinische Entscheidungsfindung mit Künstlicher Intelligenz: Ein interdisziplinärer Governance-Ansatz</title><link>https://dfki-nlp.github.io/publication/samhammer-2023-klinische/</link><pubDate>Tue, 02 May 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/samhammer-2023-klinische/</guid><description/></item><item><title>When performance is not enough—A multidisciplinary view on clinical decision support</title><link>https://dfki-nlp.github.io/publication/plosone2023-roller-performance/</link><pubDate>Sun, 23 Apr 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/plosone2023-roller-performance/</guid><description/></item><item><title>Automatic Extraction of Medication Mentions from Tweets—Overview of the BioCreative VII Shared Task 3 Competition</title><link>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</link><pubDate>Wed, 15 Feb 2023 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/database2023-roller-biocreative/</guid><description/></item><item><title>''Nothing works without the doctor:'' Physicians' perception of clinical decision-making and artificial intelligence</title><link>https://dfki-nlp.github.io/publication/frontiers2022-roller-nothing/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/frontiers2022-roller-nothing/</guid><description/></item><item><title>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</title><link>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</link><pubDate>Tue, 20 Dec 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/wmt-emnlp2022-roller-findings/</guid><description/></item><item><title>Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation</title><link>https://dfki-nlp.github.io/publication/frontiers2022-roller-evaluation/</link><pubDate>Tue, 25 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/frontiers2022-roller-evaluation/</guid><description/></item><item><title>Multilingual Relation Classification via Efficient and Effective Prompting</title><link>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</link><pubDate>Sat, 22 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-chen-meffiprompt/</guid><description/></item><item><title>Full-Text Argumentation Mining on Scientific Publications</title><link>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</link><pubDate>Fri, 21 Oct 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/wiesp2022-binder-etal-full/</guid><description/></item><item><title>Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings</title><link>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</link><pubDate>Fri, 14 Oct 2022 10:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2022-ostendorff/</guid><description/></item><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item><item><title>An Annotated Corpus of Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/publication/lrec2022-roller/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-roller/</guid><description/></item><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item><item><title>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</guid><description/></item><item><title>Subjective Text Complexity Assessment for German</title><link>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-seiffe-subjective/</guid><description/></item><item><title>Claim Extraction and Law Matching for COVID-19-related Legislation</title><link>https://dfki-nlp.github.io/publication/lrec2022-dehio/</link><pubDate>Wed, 06 Jul 2022 09:37:49 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-dehio/</guid><description/></item><item><title>Specialized Document Embeddings for Aspect-based Similarity of Research Papers</title><link>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</link><pubDate>Wed, 06 Jul 2022 09:33:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/jcdl2022-ostendorff/</guid><description/></item><item><title>HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information</title><link>https://dfki-nlp.github.io/publication/acl2022-ruan/</link><pubDate>Wed, 06 Jul 2022 09:25:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-ruan/</guid><description/></item><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item><item><title>A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition</title><link>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-repl4nlp-chen-fewie/</guid><description/></item><item><title>Why only Micro-$F_1$? Class Weighting of Measures for Relation Classification</title><link>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</link><pubDate>Thu, 26 May 2022 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-nlppower-harbecke-f1/</guid><description/></item><item><title>Detecting Covariate Drift with Explanations</title><link>https://dfki-nlp.github.io/publication/nlpcc-castle/</link><pubDate>Fri, 15 Oct 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/nlpcc-castle/</guid><description/></item><item><title>MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</link><pubDate>Tue, 07 Sep 2021 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/konvens2021-hennig-mobie/</guid><description/></item><item><title>Evaluating Document Representations for Content-based Legal Literature Recommendations</title><link>https://dfki-nlp.github.io/publication/ostendorff2021/</link><pubDate>Thu, 27 May 2021 12:18:03 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2021/</guid><description/></item><item><title>Aspect-based Document Similarity for Research Papers</title><link>https://dfki-nlp.github.io/publication/ostendorff2020c/</link><pubDate>Sun, 27 Dec 2020 12:17:32 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020c/</guid><description/></item><item><title>Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction</title><link>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/semeval2020-huebner-defx/</guid><description>&lt;p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.&lt;/p></description></item><item><title>Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles</title><link>https://dfki-nlp.github.io/publication/ostendorff2020/</link><pubDate>Thu, 27 Aug 2020 12:17:42 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2020/</guid><description/></item><item><title>Pattern-Guided Integrated Gradients</title><link>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</link><pubDate>Wed, 29 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/icml-schwarzenberg-castle/</guid><description/></item><item><title>Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning</title><link>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-ecnlp2020-zhang-bootstrapping/</guid><description/></item><item><title>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</title><link>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</guid><description/></item><item><title>Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-probing/</guid><description/></item><item><title>TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task</title><link>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2020-alt-tacred-revisited/</guid><description/></item><item><title>Evaluating German Transformer Language Models with Syntactic Agreement Tests</title><link>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</link><pubDate>Tue, 23 Jun 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/swisstext2020-zaczynska-evaluating/</guid><description/></item><item><title>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</title><link>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</link><pubDate>Sat, 16 May 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2020-aksenov-abstractive/</guid><description/></item><item><title>Cross-lingual Neural Vector Conceptualization</title><link>https://dfki-nlp.github.io/publication/cl-nvc/</link><pubDate>Mon, 30 Sep 2019 18:09:58 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/cl-nvc/</guid><description/></item><item><title>Layerwise Relevance Visualization in Convolutional Text Graph Classifiers</title><link>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</link><pubDate>Thu, 26 Sep 2019 07:38:43 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/</guid><description/></item><item><title>Fine-Tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction</title><link>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</link><pubDate>Mon, 29 Jul 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2019-alt-finetuning/</guid><description/></item><item><title>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</title><link>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</link><pubDate>Mon, 24 Jun 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/qomex2019-iskender-crowd/</guid><description/></item><item><title>Enriching BERT with Knowledge Graph Embedding for Document Classification</title><link>https://dfki-nlp.github.io/publication/ostendorff2019/</link><pubDate>Mon, 27 May 2019 12:32:18 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/ostendorff2019/</guid><description/></item><item><title>Improving Relation Extraction by Pre-Trained Language Representations</title><link>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</link><pubDate>Mon, 20 May 2019 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/akbc2019-alt-improving/</guid><description/></item><item><title>Neural Vector Conceptualization for Word Vector Space Interpretation</title><link>https://dfki-nlp.github.io/publication/nvc/</link><pubDate>Tue, 02 Apr 2019 17:55:21 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/nvc/</guid><description/></item><item><title>Train, Sort, Explain: Learning to Diagnose Translation Models</title><link>https://dfki-nlp.github.io/publication/diamat/</link><pubDate>Thu, 28 Mar 2019 17:40:45 +0100</pubDate><guid>https://dfki-nlp.github.io/publication/diamat/</guid><description/></item><item><title>mEx - an Information Extraction Platform for German Medical Text</title><link>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</link><pubDate>Sat, 01 Dec 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/mex-an-information-extraction-platform-for-german-medical-text/</guid><description/></item><item><title>Learning Explanations From Language Data</title><link>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</link><pubDate>Fri, 02 Nov 2018 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/learning-explanations-from-language-data/</guid><description/></item></channel></rss>
\ No newline at end of file
diff --git a/publication/jcdl2022-ostendorff/index.html b/publication/jcdl2022-ostendorff/index.html
index f7b9463b..f2e69dfd 100644
--- a/publication/jcdl2022-ostendorff/index.html
+++ b/publication/jcdl2022-ostendorff/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/malteos/aspect-document-embeddings target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.1145/3529372.3530912 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Document embeddings and similarity measures underpin content-based recommender systems, whereby a document is commonly represented as a single generic embedding. However, similarity computed on single vector representations provides only one perspective on document similarity that ignores which aspects make two documents alike. To address this limitation, aspect-based similarity measures have been developed using document segmentation or pairwise multi-class document classification. While segmentation harms the document coherence, the pairwise classification approach scales poorly to large scale corpora. In this paper, we treat aspect-based similarity as a classical vector similarity problem in aspect-specific embedding spaces. We represent a document not as a single generic embedding but as multiple specialized embeddings. Our approach avoids document segmentation and scales linearly w.r.t. the corpus size. In an empirical study, we use the Papers with Code corpus containing 157, 606 research papers and consider the task, method, and dataset of the respective research papers as their aspects. We compare and analyze three generic document embeddings, six specialized document embeddings and a pairwise classification baseline in the context of research paper recommendations. As generic document embeddings, we consider FastText, SciBERT, and SPECTER. To compute the specialized document embeddings, we compare three alternative methods inspired by retrofitting, fine-tuning, and Siamese networks. In our experiments, Siamese SciBERT achieved the highest scores. Additional analyses indicate an implicit bias of the generic document embeddings towards the dataset aspect and against the method aspect of each research paper. Our approach of aspect-based document embeddings mitigates potential risks arising from implicit biases by making them explicit. This can, for example, be used for more diverse and explainable recommendations.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the ACM/IEEE Joint Conference on Digital Libraries 2022</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
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diff --git a/publication/konvens2021-hennig-mobie/index.html b/publication/konvens2021-hennig-mobie/index.html
index 6688ca1b..786cd998 100644
--- a/publication/konvens2021-hennig-mobie/index.html
+++ b/publication/konvens2021-hennig-mobie/index.html
@@ -7,7 +7,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/MobIE/ target=_blank rel=noopener>Dataset</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/cora4nlp/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. To the best of our knowledge, this is the first German-language dataset that combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks. We make MobIE public at <a href=https://github.com/dfki-nlp/mobie>https://github.com/dfki-nlp/mobie</a>.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021): Short Papers</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/aleksandra-gabryszak/><img class="avatar mr-3 avatar-circle" src=../../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Aleksandra Gabryszak"></a><div class=media-body><h5 class=card-title><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/publication/konvens2023-binsumait-etal-factuality/index.html b/publication/konvens2023-binsumait-etal-factuality/index.html
index f13a03b5..04dcd018 100644
--- a/publication/konvens2023-binsumait-etal-factuality/index.html
+++ b/publication/konvens2023-binsumait-etal-factuality/index.html
@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Factuality Detection using Machine Translation - a Use Case for German Clinical Text</h1><div class=article-metadata><div><span><a href=../../authors/mohammed-bin-sumait/>Mohammed Bin Sumait</a></span>, <span><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/roland-roller/>Roland Roller</a></span></div><span class=article-date>August 2023</span></div><div class="btn-links mb-3"><a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/konvens2023-binsumait-etal-factuality/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/cora4nlp/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed. In most cases, a sufficient number of examples is necessary to handle such phenomena in a supervised machine learning setting. However, as clinical text might contain sensitive information, data cannot be easily shared. In the context of factuality detection, this work presents a simple solution using machine translation to translate English data to German to train a transformer-based factuality detection model.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/aleksandra-gabryszak/><img class="avatar mr-3 avatar-circle" src=../../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Aleksandra Gabryszak"></a><div class=media-body><h5 class=card-title><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/index.html b/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/index.html
index 615a37bc..186211a5 100644
--- a/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/index.html
+++ b/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/layerwise-relevance-visualization-in-convolutional-text-graph-classifiers/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/lrv target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. Graph Convolutional Networks (GCN) allow this projection, but existing explainability methods do not exploit this fact, i.e. do not focus their explanations on intermediate states. In this work, we present a novel method that traces and visualizes features that contribute to a classification decision in the visible and hidden layers of a GCN. Our method exposes hidden cross-layer dynamics in the input graph structure. We experimentally demonstrate that it yields meaningful layerwise explanations for a GCN sentence classifier.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the EMNLP 2019 Workshop on Graph-Based Natural Language Processing</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/learning-explanations-from-language-data/index.html b/publication/learning-explanations-from-language-data/index.html
index a3be15b3..43a1a973 100644
--- a/publication/learning-explanations-from-language-data/index.html
+++ b/publication/learning-explanations-from-language-data/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/learning-explanations-from-language-data/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/language-attributions target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>PatternAttribution is a recent method, introduced in the vision domain, that explains classifications of deep neural networks. We demonstrate that it also generates meaningful interpretations in the language domain.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2020-aksenov-abstractive/index.html b/publication/lrec2020-aksenov-abstractive/index.html
index 20e71732..93faec5d 100644
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling</h1><div class=article-metadata><div><span><a href=../../authors/dmitrii-aksenov/>Dmitrii Aksenov</a></span>, <span><a href=../../authors/julian-moreno-schneider/>Julian Moreno Schneider</a></span>, <span><a href=../../authors/peter-bourgonje/>Peter Bourgonje</a></span>, <span><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/georg-rehm/>Georg Rehm</a></span></div><span class=article-date>May 2020</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://www.aclweb.org/anthology/2020.lrec-1.825.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/lrec2020-aksenov-abstractive/cite.bib>Cite</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. We also explore how locality modeling, i.e., the explicit restriction of calculations to the local context, can affect the summarization ability of the Transformer. This is done by introducing 2-dimensional convolutional self-attention into the first layers of the encoder. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset. We additionally train our model on the SwissText dataset to demonstrate usability on German. Both models outperform the baseline in ROUGE scores on two datasets and show its superiority in a manual qualitative analysis.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2022-calizzano/index.html b/publication/lrec2022-calizzano/index.html
index ae8a7dc9..79edc75a 100644
--- a/publication/lrec2022-calizzano/index.html
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 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/lrec2022-calizzano/cite.bib>Cite</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended summaries of approx. 11 sentences. The dataset comprises 39,626 summaries which are news articles from Wikinews and their sources. We compare three multilingual transformer models on the extractive summarisation task and three training scenarios on which we fine-tune mT5 to perform abstractive summarisation. This results in strong baselines for both extractive and abstractive summarisation on WikinewsSum. We also show how the combination of an extractive model with an abstractive one can be used to create extended abstractive summaries from long input documents. Finally, our results show that fine-tuning mT5 on all the languages combined significantly improves the summarisation performance on low-resource languages.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/summarization/>summarization</a>
 <a class="badge badge-light" href=../../tag/wikinews/>wikinews</a></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2022-dehio/index.html b/publication/lrec2022-dehio/index.html
index 8652dd94..195fab3e 100644
--- a/publication/lrec2022-dehio/index.html
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 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/lrec2022-dehio/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/covid19-law-matching target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>To cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference underlying laws. We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws. We examine the feasibility of the two tasks concerning claims about COVID-19-related laws from Berlin, Germany. For both tasks, we create and make publicly available the data sets and report the results of initial experiments. We obtain promising results with Transformer-based models that achieve 46.7 F1 for claim extraction and 91.4 F1 for law matching, albeit with some conceptual limitations. Furthermore, we discuss challenges of current machine learning approaches for legal language processing and their ability for complex legal reasoning tasks.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2022-gabryszak/index.html b/publication/lrec2022-gabryszak/index.html
index 2fe1046d..e631925a 100644
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 <a class="btn btn-outline-primary btn-page-header" href=../../project/sim3s/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children. The data can be used to identify hurdles and improve travel planning for vulnerable passengers, as well as to monitor a perception of transportation businesses regarding the social inclusion of all passengers. The data is publicly available under: <a href=https://github.com/DFKI-NLP/sim3s-corpus>https://github.com/DFKI-NLP/sim3s-corpus</a></p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/sentiment-analysis/>Sentiment Analysis</a>
 <a class="badge badge-light" href=../../tag/barrier-free-travel/>barrier-free travel</a></div><div class="media author-card content-widget-hr"><a href=../../authors/aleksandra-gabryszak/><img class="avatar mr-3 avatar-circle" src=../../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Aleksandra Gabryszak"></a><div class=media-body><h5 class=card-title><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/philippe-thomas/><img class="avatar mr-3 avatar-circle" src=../../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt="Philippe Thomas"></a><div class=media-body><h5 class=card-title><a href=../../authors/philippe-thomas/>Philippe Thomas</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../dataset/dfki-mobasa-corpus/>MobASA Corpus</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2022-macketanz-testsuite/index.html b/publication/lrec2022-macketanz-testsuite/index.html
index 69dbe883..22dd4432 100644
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 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/lrec2022-macketanz-testsuite/cite.bib>Cite</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>This paper presents a fine-grained test suite for the language pair German–English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We describe the creation and implementation of the test suite in detail, providing a full list of all categories and phenomena. Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese–English. We describe how we tracked the development of the performance of various systems MT systems over the years with the help of the test suite and which categories and phenomena are prone to resulting in MT errors. For the first time, we also make a large part of our test suite publicly available to the research community.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the Thirteenth Language Resources and Evaluation Conference</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/machine-translation/>Machine Translation</a>
 <a class="badge badge-light" href=../../tag/linguistic-test-suite/>linguistic test suite</a></div><div class="media author-card content-widget-hr"><a href=../../authors/eleftherios-avramidis/><img class="avatar mr-3 avatar-circle" src=../../authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt="Eleftherios Avramidis"></a><div class=media-body><h5 class=card-title><a href=../../authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/he-wang/><img class="avatar mr-3 avatar-circle" src=../../authors/he-wang/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="He Wang"></a><div class=media-body><h5 class=card-title><a href=../../authors/he-wang/>He Wang</a></h5><h6 class=card-subtitle>Software Engineer</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:he.wang@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/wanghe001 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../publication/vandeghinste-2023-ele-sign/>European Language Equality, Report on Europe's Sign Languages</a></li><li><a href=../../publication/acl2023-zhu-sign/>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2022-raithel-cross-adr/index.html b/publication/lrec2022-raithel-cross-adr/index.html
index 4ec87755..22238e30 100644
--- a/publication/lrec2022-raithel-cross-adr/index.html
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 <a class="badge badge-light" href=../../tag/text-classification/>text classification</a>
 <a class="badge badge-light" href=../../tag/adverse-drug-reactions/>adverse drug reactions</a></div><div class="media author-card content-widget-hr"><a href=../../authors/lisa-raithel/><img class="avatar mr-3 avatar-circle" src=../../authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt="Lisa Raithel"></a><div class=media-body><h5 class=card-title><a href=../../authors/lisa-raithel/>Lisa Raithel</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.linkedin.com/in/lisa-raithel-3b2865240/ target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/philippe-thomas/><img class="avatar mr-3 avatar-circle" src=../../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt="Philippe Thomas"></a><div class=media-body><h5 class=card-title><a href=../../authors/philippe-thomas/>Philippe Thomas</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2022-roller/index.html b/publication/lrec2022-roller/index.html
index 1408bfe5..6d59206c 100644
--- a/publication/lrec2022-roller/index.html
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@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>An Annotated Corpus of Textual Explanations for Clinical Decision Support</h1><div class=article-metadata><div><span><a href=../../authors/roland-roller/>Roland Roller</a></span>, <span><a href=../../authors/aljoscha-burchardt/>Aljoscha Burchardt</a></span>, <span><a href=../../authors/nils-feldhus/>Nils Feldhus</a></span>, <span><a href=../../authors/laura-seiffe/>Laura Seiffe</a></span>, <span><a href=../../authors/klemens-budde/>Klemens Budde</a></span>, <span><a href=../../authors/simon-ronicke/>Simon Ronicke</a></span>, <span><a href=../../authors/bilgin-osmanodja/>Bilgin Osmanodja</a></span></div><span class=article-date>August 2022</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://aclanthology.org/2022.lrec-1.248.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/lrec2022-roller/cite.bib>Cite</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>In recent years, machine learning for clinical decision support has gained more and more attention. In order to introduce such applications into clinical practice, a good performance might be essential, however, the aspect of trust should not be underestimated. For the treating physician using such a system and being (legally) responsible for the decision made, it is particularly important to understand the system’s recommendation. To provide insights into a model’s decision, various techniques from the field of explainability (XAI) have been proposed whose output is often enough not targeted to the domain experts that want to use the model. To close this gap, in this work, we explore how explanations could possibly look like in future. To this end, this work presents a dataset of textual explanations in context of decision support. Within a reader study, human physicians estimated the likelihood of possible negative patient outcomes in the near future and justified each decision with a few sentences. Using those sentences, we created a novel corpus, annotated with different semantic layers. Moreover, we provide an analysis of how those explanations are constructed, and how they change depending on physician, on the estimated risk and also in comparison to an automatic clinical decision support system with feature importance.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/nils-feldhus/><img class="avatar mr-3 avatar-circle" src=../../authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt="Nils Feldhus"></a><div class=media-body><h5 class=card-title><a href=../../authors/nils-feldhus/>Nils Feldhus</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/lrec2022-seiffe-subjective/index.html b/publication/lrec2022-seiffe-subjective/index.html
index 96f7300a..0d4e98a3 100644
--- a/publication/lrec2022-seiffe-subjective/index.html
+++ b/publication/lrec2022-seiffe-subjective/index.html
@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Subjective Text Complexity Assessment for German</h1><div class=article-metadata><div><span><a href=../../authors/laura-seiffe/>Laura Seiffe</a></span>, <span><a href=../../authors/fares-kallel/>Fares Kallel</a></span>, <span><a href=../../authors/sebastian-moller/>Sebastian Möller</a></span>, <span><a href=../../authors/babak-naderi/>Babak Naderi</a></span>, <span><a href=../../authors/roland-roller/>Roland Roller</a></span></div><span class=article-date>August 2022</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://aclanthology.org/2022.lrec-1.74.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/lrec2022-seiffe-subjective/cite.bib>Cite</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>For different reasons, text can be difficult to read and understand for many people, especially if the text’s language is too complex. In order to provide suitable text for the target audience, it is necessary to measure its complexity. In this paper we describe subjective experiments to assess the readability of German text. We compile a new corpus of sentences provided by a German IT service provider. The sentences are annotated with the subjective complexity ratings by two groups of participants, namely experts and non-experts for that text domain. We then extract an extensive set of linguistically motivated features that are supposedly interacting with complexity perception. We show that a linear regression model with a subset of these features can be a very good predictor of text complexity.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/mex-an-information-extraction-platform-for-german-medical-text/index.html b/publication/mex-an-information-extraction-platform-for-german-medical-text/index.html
index c83d4974..bd0dfd5f 100644
--- a/publication/mex-an-information-extraction-platform-for-german-medical-text/index.html
+++ b/publication/mex-an-information-extraction-platform-for-german-medical-text/index.html
@@ -7,7 +7,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://figshare.com/articles/SWAT4HCLS_mEx_InformationExtractionPlatform_pdf/7436480/2 target=_blank rel=noopener>Poster</a>
 <a class="btn btn-outline-primary btn-page-header" href=http://biomedical.dfki.de/mEx target=_blank rel=noopener>Demo</a></div></div><div class=article-container><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 11th International Conference on Semantic Web Applications and Tools for Healthcare and Life Sciences (SWAT4HCLS'2018)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/he-wang/><img class="avatar mr-3 avatar-circle" src=../../authors/he-wang/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="He Wang"></a><div class=media-body><h5 class=card-title><a href=../../authors/he-wang/>He Wang</a></h5><h6 class=card-subtitle>Software Engineer</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:he.wang@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/wanghe001 target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/nlpcc-castle/index.html b/publication/nlpcc-castle/index.html
index cfadd36d..fa4a76e1 100644
--- a/publication/nlpcc-castle/index.html
+++ b/publication/nlpcc-castle/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=../../project/plass/>Project</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.1007/978-3-030-88483-3_24 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Detecting when there is a domain drift between training and inference data is important for any model evaluated on data collected in real time. Many current data drift detection methods only utilize input features to detect domain drift. While effective, these methods disregard the model’s evaluation of the data, which may be a significant source of information about the data domain. We propose to use information from the model in the form of explanations, specifically gradient times input, in order to utilize this information. Following the framework of Rabanser et al. [11], we combine these explanations with two-sample tests in order to detect a shift in distribution between training and evaluation data. Promising initial experiments show that explanations provide useful information for detecting shift, which potentially improves upon the current state-of-the-art.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of: Natural Language Processing and Chinese Computing, 10th CCF International Conference, NLPCC 2021</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/steffen-castle/><img class="avatar mr-3 avatar-circle" src=../../authors/steffen-castle/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Steffen Castle"></a><div class=media-body><h5 class=card-title><a href=../../authors/steffen-castle/>Steffen Castle</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:steffen.castle@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/grappli target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/nvc/index.html b/publication/nvc/index.html
index 543b5e4a..3cbbdc93 100644
--- a/publication/nvc/index.html
+++ b/publication/nvc/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/nvc/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/dfki-nlp/nvc target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, we train a neural model to conceptualize word vectors, which means that it activates higher order concepts it recognizes in a given vector. Contrary to prior approaches, our model operates in the original vector space and is capable of learning non-linear relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">NAACL 2019 Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/lisa-raithel/><img class="avatar mr-3 avatar-circle" src=../../authors/lisa-raithel/avatar_hudbba48612f22fb5ebb8c647053294c65_234475_270x270_fill_q90_lanczos_center.jpg alt="Lisa Raithel"></a><div class=media-body><h5 class=card-title><a href=../../authors/lisa-raithel/>Lisa Raithel</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:lisa.raithel@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?hl=de&user=S0TC4zMAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lraithel target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.linkedin.com/in/lisa-raithel-3b2865240/ target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/david-harbecke/><img class="avatar mr-3 avatar-circle" src=../../authors/david-harbecke/avatar_hu87a732928d9f1921015e73de9f3f9ceb_50360_270x270_fill_q90_lanczos_center.jpg alt="David Harbecke"></a><div class=media-body><h5 class=card-title><a href=../../authors/david-harbecke/>David Harbecke</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:david.harbecke@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=km2yuA4AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/harbecke target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/ostendorff2019/index.html b/publication/ostendorff2019/index.html
index 5618cda8..323fff82 100644
--- a/publication/ostendorff2019/index.html
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@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Enriching BERT with Knowledge Graph Embedding for Document Classification</h1><div class=article-metadata><div><span><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></span>, <span><a href=../../authors/peter-bourgonje/>Peter Bourgonje</a></span>, <span><a href=../../authors/maria-berger/>Maria Berger</a></span>, <span><a href=../../authors/julian-moreno-schneider/>Julian Moreno Schneider</a></span>, <span><a href=../../authors/georg-rehm/>Georg Rehm</a></span>, <span><a href=../../authors/bela-gipp/>Bela Gipp</a></span></div><span class=article-date>May 2019</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://ostendorff.org/assets/pdf/ostendorff2019.pdf target=_blank rel=noopener>PDF</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/malteos/pytorch-bert-document-classification target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classification task. For a more coarse-grained classification using eight labels we achieve an F1- score of 87.20, while a detailed classification using 343 labels yields an F1-score of 64.70. We make the source code and trained models of our experiments publicly available</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">GermEval Workshop located at KONVENS 2019</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/ostendorff2020/index.html b/publication/ostendorff2020/index.html
index 70fc968b..9c9df4a5 100644
--- a/publication/ostendorff2020/index.html
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@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/malteos/semantic-document-relations target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.1145/3383583.3398525 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Many digital libraries recommend literature to their users considering the similarity between a query document and their repository. However, they often fail to distinguish what is the relationship that makes two documents alike. In this paper, we model the problem of finding the relationship between two documents as a pairwise document classification task. To find the semantic relation between documents, we apply a series of techniques, such as GloVe, Paragraph-Vectors, BERT, and XLNet under different configurations (e.g., sequence length, vector concatenation scheme), including a Siamese architecture for the Transformer-based systems. We perform our experiments on a newly proposed dataset of 32,168 Wikipedia article pairs and Wikidata properties that define the semantic document relations. Our results show vanilla BERT as the best performing system with an F1-score of 0.93, which we manually examine to better understand its applicability to other domains. Our findings suggest that classifying semantic relations between documents is a solvable task and motivates the development of recommender systems based on the evaluated techniques. The discussions in this paper serve as first steps in the exploration of documents through SPARQL-like queries such that one could find documents that are similar in one aspect but dissimilar in another.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the ACM/IEEE Joint Conference on Digital Libraries</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/ostendorff2020c/index.html b/publication/ostendorff2020c/index.html
index 85969f36..c0b86b2c 100644
--- a/publication/ostendorff2020c/index.html
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@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/malteos/aspect-document-similarity target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.18653/v1/2020.coling-main.545 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of applications like recommender systems that rely on document similarity. In this paper, we extend similarity with aspect information by performing a pairwise document classification task. We evaluate our aspect-based document similarity for research papers. Paper citations indicate the aspect-based similarity, i.e., the section title in which a citation occurs acts as a label for the pair of citing and cited paper. We apply a series of Transformer models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. Our results show SciBERT as the best performing system. A qualitative examination validates our quantitative results. Our findings motivate future research of aspect-based document similarity and the development of a recommender system based on the evaluated techniques. We make our datasets, code, and trained models publicly available.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 28th International Conference on Computational Linguistics</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/ostendorff2021/index.html b/publication/ostendorff2021/index.html
index 3214f2f4..158072de 100644
--- a/publication/ostendorff2021/index.html
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@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Evaluating Document Representations for Content-based Legal Literature Recommendations</h1><div class=article-metadata><div><span><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></span>, <span><a href=../../authors/elliott-ash/>Elliott Ash</a></span>, <span><a href=../../authors/terry-ruas/>Terry Ruas</a></span>, <span><a href=../../authors/bela-gipp/>Bela Gipp</a></span>, <span><a href=../../authors/julian-moreno-schneider/>Julian Moreno Schneider</a></span>, <span><a href=../../authors/georg-rehm/>Georg Rehm</a></span></div><span class=article-date>May 2021</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://ostendorff.org/assets/pdf/ostendorff2021.pdf target=_blank rel=noopener>PDF</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/malteos/legal-document-similarity/ target=_blank rel=noopener>Code</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflect the latest advances in recommender systems and representation learning research. Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets. Thus, these studies have limited reproducibility. To address the gap between research and practice, we explore a set of state-of-the-art document representation methods for the task of retrieving semantically related US case law. We evaluate text-based (e.g., fastText, Transformers), citation-based (e.g., DeepWalk, Poincaré), and hybrid methods. We compare in total 27 methods using two silver standards with annotations for 2,964 documents. The silver standards are newly created from Open Case Book and Wikisource and can be reused under an open license facilitating reproducibility. Our experiments show that document representations from averaged fastText word vectors (trained on legal corpora) yield the best results, closely followed by Poincaré citation embeddings. Combining fastText and Poincaré in a hybrid manner further improves the overall result. Besides the overall performance, we analyze the methods depending on document length, citation count, and the coverage of their recommendations. We make our source code, models, and datasets publicly available at this https URL.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the 18th International Conference on Artificial Intelligence and Law</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/plosone2023-roller-performance/index.html b/publication/plosone2023-roller-performance/index.html
index cba7b2cf..09163c60 100644
--- a/publication/plosone2023-roller-performance/index.html
+++ b/publication/plosone2023-roller-performance/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/plosone2023-roller-performance/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.1371/journal.pone.0282619 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#2>Journal article</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">PLOS ONE</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/pml4dc-2023-ostendorff/index.html b/publication/pml4dc-2023-ostendorff/index.html
index 93daaaab..74603778 100644
--- a/publication/pml4dc-2023-ostendorff/index.html
+++ b/publication/pml4dc-2023-ostendorff/index.html
@@ -5,7 +5,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/malteos/clp-transfer target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.48550/arXiv.2301.09626 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Most Transformer language models are primarily pretrained on English text, limiting their use for other languages. As the model sizes grow, the performance gap between English and other languages with fewer compute and data resources increases even further. Consequently, more resource-efficient training methods are needed to bridge the gap for languages with fewer resources available. To address this problem, we introduce a cross-lingual and progressive transfer learning approach, called CLP-Transfer, that transfers models from a source language, for which pretrained models are publicly available, like English, to a new target language. As opposed to prior work, which focused on the cross-lingual transfer between two languages, we extend the transfer to the model size. Given a pretrained model in a source language, we aim for a same-sized model in a target language. Instead of training a model from scratch, we exploit a smaller model that is in the target language but requires much fewer resources. Both small and source models are then used to initialize the token embeddings of the larger model based on the overlapping vocabulary of the source and target language. All remaining weights are reused from the model in the source language. This approach outperforms the sole cross-lingual transfer and can save up to 80% of the training steps compared to the random initialization.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the PML4DC workshop co-located with ICLR 2023</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/malte-ostendorff/><img class="avatar mr-3 avatar-circle" src=../../authors/malte-ostendorff/avatar_hu77092bd565a115ebe8238004796c5b72_51670_270x270_fill_q90_lanczos_center.jpg alt="Malte Ostendorff"></a><div class=media-body><h5 class=card-title><a href=../../authors/malte-ostendorff/>Malte Ostendorff</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:malte.ostendorffg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://twitter.com/xyou target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href="https://scholar.google.de/citations?user=8WfhSIcAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/malteos target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://ostendorff.org target=_blank rel=noopener><i class="fas fa-link"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/qomex2019-iskender-crowd/index.html b/publication/qomex2019-iskender-crowd/index.html
index 390d5362..be8b48fc 100644
--- a/publication/qomex2019-iskender-crowd/index.html
+++ b/publication/qomex2019-iskender-crowd/index.html
@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>A Crowdsourcing Approach to Evaluate the Quality of Query-based Extractive Text Summaries</h1><div class=article-metadata><div><span><a href=../../authors/neslihan-iskender/>Neslihan Iskender</a></span>, <span><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></span>, <span><a href=../../authors/tim-polzehl/>Tim Polzehl</a></span>, <span><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></span>, <span><a href=../../authors/sebastian-moller/>Sebastian Möller</a></span></div><span class=article-date>June 2019</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://ieeexplore.ieee.org/document/8743174 target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/qomex2019-iskender-crowd/cite.bib>Cite</a></div></div><div class=article-container><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/aleksandra-gabryszak/><img class="avatar mr-3 avatar-circle" src=../../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Aleksandra Gabryszak"></a><div class=media-body><h5 class=card-title><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/samhammer-2023-klinische/index.html b/publication/samhammer-2023-klinische/index.html
index c9c2deb0..398a270c 100644
--- a/publication/samhammer-2023-klinische/index.html
+++ b/publication/samhammer-2023-klinische/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/samhammer-2023-klinische/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.1007/978-3-662-67008-8 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Dieses Open-Access-essential schafft Orientierung, wenn Künstliche Intelligenz im klinischen Alltag eingesetzt wird. Die Herausforderungen werden anhand zweier Beispiele aus dem Bereich der Nephrologie erläutert, die ethisch und rechtlich reflektiert werden. Ein umfangreicher Empfehlungsteil schließt diesen durchweg interdisziplinär erarbeiteten Band ab.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#5>Book</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">essentials</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/semeval2020-huebner-defx/index.html b/publication/semeval2020-huebner-defx/index.html
index 01b31562..c112ce28 100644
--- a/publication/semeval2020-huebner-defx/index.html
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@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/defx target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/plass/>Project</a></div></div><div class=article-container><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the Fourteenth Workshop on Semantic Evaluation</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style><p>Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.</p></div><div class="media author-card content-widget-hr"><a href=../../authors/christoph-alt/><img class="avatar mr-3 avatar-circle" src=../../authors/christoph-alt/avatar_hu6840e090b4b222e0567db036b6b5aa87_327354_270x270_fill_q90_lanczos_center.jpg alt="Christoph Alt"></a><div class=media-body><h5 class=card-title><a href=../../authors/christoph-alt/>Christoph Alt</a></h5><ul class=network-icon aria-hidden=true><li><a href=../../#contact><i class="fas fa-envelope"></i></a></li><li><a href=https://www.linkedin.com/in/ChristophAlt target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href="https://scholar.google.com/citations?user=JiJIrfwAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ChristophAlt target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication/shti2023-abdulnazar-sapbert/index.html b/publication/shti2023-abdulnazar-sapbert/index.html
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@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/shti2023-abdulnazar-sapbert/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.3233/SHTI230278 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized representations have been very successful recently. In this work, we analyze the impact of contextualized and non-contextualized embeddings for medical concept normalization, mapping clinical terms via a k-NN approach to SNOMED CT. The non-contextualized concept mapping resulted in a much better performance (F1-score = 0.853) than the contextualized representation (F1-score = 0.322).</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#2>Journal article</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Studies in Health Technology and Informatics</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/swisstext2020-zaczynska-evaluating/index.html b/publication/swisstext2020-zaczynska-evaluating/index.html
index ff4ffa64..7ddd893a 100644
--- a/publication/swisstext2020-zaczynska-evaluating/index.html
+++ b/publication/swisstext2020-zaczynska-evaluating/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/swisstext2020-zaczynska-evaluating/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/bbdc2/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Pre-trained transformer language models (TLMs) have recently refashioned natural language processing (NLP): Most stateof-the-art NLP models now operate on top of TLMs to benefit from contextualization and knowledge induction. To explain their success, the scientific community conducted numerous analyses. Besides other methods, syntactic agreement tests were utilized to analyse TLMs. Most of the studies were conducted for the English language, however. In this work, we analyse German TLMs. To this end, we design numerous agreement tasks, some of which consider peculiarities of the German language. Our experimental results show that state-of-the-art German TLMs generally perform well on agreement tasks, but we also identify and discuss syntactic structures that push them to their limits.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/karolina-zaczynska/><img class="avatar mr-3 avatar-circle" src=../../authors/karolina-zaczynska/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Karolina Zaczynska"></a><div class=media-body><h5 class=card-title><a href=../../authors/karolina-zaczynska/>Karolina Zaczynska</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:karolina.zaczynska@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/nils-feldhus/><img class="avatar mr-3 avatar-circle" src=../../authors/nils-feldhus/avatar_hu75b0d4d3ad779f26059452756b1f665c_20790_270x270_fill_q90_lanczos_center.jpg alt="Nils Feldhus"></a><div class=media-body><h5 class=card-title><a href=../../authors/nils-feldhus/>Nils Feldhus</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:nils.feldhus@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/nfelnlp target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/robert-schwarzenberg/><img class="avatar mr-3 avatar-circle" src=../../authors/robert-schwarzenberg/avatar_hu745f7a6338800fb7dbd7fe6f48f702f0_321702_270x270_fill_lanczos_center_2.png alt="Robert Schwarzenberg"></a><div class=media-body><h5 class=card-title><a href=../../authors/robert-schwarzenberg/>Robert Schwarzenberg</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:robert.schwarzenberg@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://www.semanticscholar.org/author/Robert-Schwarzenberg/1683203 target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/rbtsbg target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/aleksandra-gabryszak/><img class="avatar mr-3 avatar-circle" src=../../authors/aleksandra-gabryszak/avatar_hu52a603635ecebd45650b162dadabb4e5_12861_270x270_fill_q90_lanczos_center.jpg alt="Aleksandra Gabryszak"></a><div class=media-body><h5 class=card-title><a href=../../authors/aleksandra-gabryszak/>Aleksandra Gabryszak</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:aleksandra.gabryszak@dfki.de><i class="fas fa-envelope"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/publication/vandeghinste-2023-ele-sign/index.html b/publication/vandeghinste-2023-ele-sign/index.html
index 713556cb..849371f3 100644
--- a/publication/vandeghinste-2023-ele-sign/index.html
+++ b/publication/vandeghinste-2023-ele-sign/index.html
@@ -5,7 +5,7 @@
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/vandeghinste-2023-ele-sign/cite.bib>Cite</a>
 <a class="btn btn-outline-primary btn-page-header" href=https://doi.org/10.5281/zenodo.8047005 target=_blank rel=noopener>DOI</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>This report on Europe&rsquo;s Sign Languages is part of a series of language deliverables developed within the framework of the European Language Equality (ELE) project. The series seeks to not only delineate the current state of affairs for each European language, but to additionally identify the gaps and factors that hinder further development in research and technology. The survey presented here focuses on the condition of Language Technology (LT) with regard to Europe&rsquo;s Sign Languages, a set of languages often forgotten in the context of European Language Equality. With the rise of the deep learning paradigm in artificial intelligence, sign language technologies become technologically feasible, provided that enough data is available to feed this data-hungry paradigm. It is exactly the quality and quantity of data that is the main bottleneck in development of well performing and useful technologies. In the past, there have been several projects aimed at developing sign language technologies and methodologies that have been deemed of little value by the deaf communities. Co-creation and involvement of deaf communities throughout projects and development of technologies ensures that this does not happen again.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#6>Book section</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Project European Language Equality (ELE)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class=article-tags><a class="badge badge-light" href=../../tag/machine-translation/>Machine Translation</a></div><div class="media author-card content-widget-hr"><a href=../../authors/eleftherios-avramidis/><img class="avatar mr-3 avatar-circle" src=../../authors/eleftherios-avramidis/avatar_hue66d2e5f588239e332880e1024a730cb_17938_270x270_fill_q90_lanczos_center.jpg alt="Eleftherios Avramidis"></a><div class=media-body><h5 class=card-title><a href=../../authors/eleftherios-avramidis/>Eleftherios Avramidis</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:eleftherios.avramidis@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=HhcsbYgAAAAJ&hl=en" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/lefterav target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://www.dfki.de/en/web/about-us/employee/person/elav01/ target=_blank rel=noopener><i class="far fa-browser"></i></a></li><li><a href=https://www.researchgate.net/profile/Eleftherios-Avramidis target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/avramidis target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li><li><a href=https://twitter.com/lefterav target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="article-widget content-widget-hr"><h3>Related</h3><ul><li><a href=../../publication/acl2023-zhu-sign/>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</a></li><li><a href=../../publication/lrec2022-macketanz-testsuite/>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</a></li></ul></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
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diff --git a/publication/wiesp2022-binder-etal-full/index.html b/publication/wiesp2022-binder-etal-full/index.html
index 9fc736c5..24d0ca92 100644
--- a/publication/wiesp2022-binder-etal-full/index.html
+++ b/publication/wiesp2022-binder-etal-full/index.html
@@ -6,7 +6,7 @@
 <a class="btn btn-outline-primary btn-page-header" href=https://github.com/DFKI-NLP/sam target=_blank rel=noopener>Code</a>
 <a class="btn btn-outline-primary btn-page-header" href=../../project/cora4nlp/>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the First Workshop on Information Extraction from Scientific Publications</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/arne-binder/><img class="avatar mr-3 avatar-circle" src=../../authors/arne-binder/avatar_huf1b341a11428bbdba6a06391dba9a6b4_16118_270x270_fill_q90_lanczos_center.jpg alt="Arne Binder"></a><div class=media-body><h5 class=card-title><a href=../../authors/arne-binder/>Arne Binder</a></h5><h6 class=card-subtitle>PhD Candidate</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:arne.binder@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href=https://github.com/ArneBinder target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/leonhard-hennig/><img class="avatar mr-3 avatar-circle" src=../../authors/leonhard-hennig/avatar_hu0bd7dae612fe545036f91d2e2e11a312_931324_270x270_fill_lanczos_center_2.png alt="Leonhard Hennig"></a><div class=media-body><h5 class=card-title><a href=../../authors/leonhard-hennig/>Leonhard Hennig</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:leonhard.hennig@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=V9G-FOoAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/leonhardhennig target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/publication/wmt-emnlp2022-roller-findings/index.html b/publication/wmt-emnlp2022-roller-findings/index.html
index f0d9d407..46e3bda3 100644
--- a/publication/wmt-emnlp2022-roller-findings/index.html
+++ b/publication/wmt-emnlp2022-roller-findings/index.html
@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports</h1><div class=article-metadata><div><span><a href=../../authors/mariana-neves/>Mariana Neves</a></span>, <span><a href=../../authors/antonio-jimeno-yepes/>Antonio Jimeno Yepes</a></span>, <span><a href=../../authors/amy-siu/>Amy Siu</a></span>, <span><a href=../../authors/roland-roller/>Roland Roller</a></span>, <span><a href=../../authors/philippe-thomas/>Philippe Thomas</a></span>, <span><a href=../../authors/maika-vicente-navarro/>Maika Vicente Navarro</a></span>, <span><a href=../../authors/lana-yeganova/>Lana Yeganova</a></span>, <span><a href=../../authors/dina-wiemann/>Dina Wiemann</a></span>, <span><a href=../../authors/giorgio-maria-di-nunzio/>Giorgio Maria Di Nunzio</a></span>, <span><a href=../../authors/federica-vezzani/>Federica Vezzani</a></span>, <span><a href=../../authors/christel-gerardin/>Christel Gerardin</a></span>, <span><a href=../../authors/rachel-bawden/>Rachel Bawden</a></span>, <span><a href=../../authors/darryl-johan-estrada/>Darryl Johan Estrada</a></span>, <span><a href=../../authors/salvador-lima-lopez/>Salvador Lima-lopez</a></span>, <span><a href=../../authors/eulalia-farre-maduel/>Eulalia Farre-maduel</a></span>, <span><a href=../../authors/martin-krallinger/>Martin Krallinger</a></span>, <span><a href=../../authors/cristian-grozea/>Cristian Grozea</a></span>, <span><a href=../../authors/aurelie-neveol/>Aurelie Neveol</a></span></div><span class=article-date>December 2022</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://aclanthology.org/2022.wmt-1.69.pdf target=_blank rel=noopener>PDF</a>
 <a href=# class="btn btn-outline-primary btn-page-header js-cite-modal" data-filename=/publication/wmt-emnlp2022-roller-findings/cite.bib>Cite</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>In the seventh edition of the WMT Biomedical Task, we addressed a total of seven languagepairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian. This year{'}s test sets covered three types of biomedical text genre. In addition to scientific abstracts and terminology items used in previous editions, we released test sets of clinical cases. The evaluation of clinical cases translations were given special attention by involving clinicians in the preparation of reference translations and manual evaluation. For the main MEDLINE test sets, we received a total of 609 submissions from 37 teams. For the ClinSpEn sub-task, we had the participation of five teams.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=../../publication/#1>Conference paper</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9">Proceedings of the Seventh Conference on Machine Translation (WMT)</div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style></div><div class="media author-card content-widget-hr"><a href=../../authors/roland-roller/><img class="avatar mr-3 avatar-circle" src=../../authors/roland-roller/avatar_hue37376a3c28dc7b4e133c1580fa30a1e_4537_270x270_fill_q90_lanczos_center.jpg alt="Roland Roller"></a><div class=media-body><h5 class=card-title><a href=../../authors/roland-roller/>Roland Roller</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:roland.roller@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=MzyKw-DOYF4C" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=../../authors/philippe-thomas/><img class="avatar mr-3 avatar-circle" src=../../authors/philippe-thomas/avatar_hu9e6c4c34209695b1778a33a66e91123d_7885_270x270_fill_q90_lanczos_center.jpg alt="Philippe Thomas"></a><div class=media-body><h5 class=card-title><a href=../../authors/philippe-thomas/>Philippe Thomas</a></h5><h6 class=card-subtitle>Senior Researcher</h6><ul class=network-icon aria-hidden=true><li><a href=mailto:philippe.thomas@dfki.de><i class="fas fa-envelope"></i></a></li><li><a href="https://scholar.google.com/citations?user=bdUBqlsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/Erechtheus target=_blank rel=noopener><i class="fab fa-github"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/publication_types/index.html b/publication_types/index.html
index a867bb39..f8b41b95 100644
--- a/publication_types/index.html
+++ b/publication_types/index.html
@@ -3,7 +3,7 @@
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diff --git a/publication_types/index.xml b/publication_types/index.xml
index 48b6f516..70830b09 100644
--- a/publication_types/index.xml
+++ b/publication_types/index.xml
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diff --git a/tag/adverse-drug-reactions/index.html b/tag/adverse-drug-reactions/index.html
index 714bad3c..1edf08b0 100644
--- a/tag/adverse-drug-reactions/index.html
+++ b/tag/adverse-drug-reactions/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>adverse drug reactions</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-raithel-cross-adr/>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/tag/adverse-drug-reactions/index.xml b/tag/adverse-drug-reactions/index.xml
index 911a9e2b..c6036a00 100644
--- a/tag/adverse-drug-reactions/index.xml
+++ b/tag/adverse-drug-reactions/index.xml
@@ -1 +1 @@
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\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>adverse drug reactions | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/adverse-drug-reactions/</link><atom:link href="https://dfki-nlp.github.io/tag/adverse-drug-reactions/index.xml" rel="self" type="application/rss+xml"/><description>adverse drug reactions</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>adverse drug reactions</title><link>https://dfki-nlp.github.io/tag/adverse-drug-reactions/</link></image><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item></channel></rss>
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diff --git a/tag/barrier-free-travel/index.html b/tag/barrier-free-travel/index.html
index 7695efe1..75abab9f 100644
--- a/tag/barrier-free-travel/index.html
+++ b/tag/barrier-free-travel/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>barrier-free travel</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-gabryszak/>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</a></h2><div class=article-style>In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/tag/barrier-free-travel/index.xml b/tag/barrier-free-travel/index.xml
index 237016da..e74f20d9 100644
--- a/tag/barrier-free-travel/index.xml
+++ b/tag/barrier-free-travel/index.xml
@@ -1 +1 @@
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>barrier-free travel | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/barrier-free-travel/</link><atom:link href="https://dfki-nlp.github.io/tag/barrier-free-travel/index.xml" rel="self" type="application/rss+xml"/><description>barrier-free travel</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>barrier-free travel</title><link>https://dfki-nlp.github.io/tag/barrier-free-travel/</link></image><item><title>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</guid><description/></item></channel></rss>
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diff --git a/tag/clinical-decision-support/index.html b/tag/clinical-decision-support/index.html
index 93e3c60c..25ef6763 100644
--- a/tag/clinical-decision-support/index.html
+++ b/tag/clinical-decision-support/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Clinical Decision Support</h1></div><div class=universal-wrapper><div><h2><a href=https://github.com/DFKI-NLP/Ex4CDS target=_blank rel=noopener>Ex4CDS - Textual Explanations for Clinical Decision Support</a></h2><div class=article-style>Ex4CDS are explanations (or more precisely justifications) of physicians in the context of clinical decision support. In the course of a larger study, physicians estimated the probability of different clinical outcomes in nephology, namely rejection, graft loss and infections, within the next 90 days. Each estimation had to be justified within a short text - these are our explanations. The explanations were provided in German and have strong similarities to general clinical notes. You can find a description and the data here: https://github.com/DFKI-NLP/Ex4CDS</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/tag/clinical-decision-support/index.xml b/tag/clinical-decision-support/index.xml
index c742a66c..40de429c 100644
--- a/tag/clinical-decision-support/index.xml
+++ b/tag/clinical-decision-support/index.xml
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Clinical Decision Support | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/clinical-decision-support/</link><atom:link href="https://dfki-nlp.github.io/tag/clinical-decision-support/index.xml" rel="self" type="application/rss+xml"/><description>Clinical Decision Support</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 01 Jul 2020 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Clinical Decision Support</title><link>https://dfki-nlp.github.io/tag/clinical-decision-support/</link></image><item><title>Ex4CDS - Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/dataset/ex4cds/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/ex4cds/</guid><description/></item></channel></rss>
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Clinical Decision Support | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/clinical-decision-support/</link><atom:link href="https://dfki-nlp.github.io/tag/clinical-decision-support/index.xml" rel="self" type="application/rss+xml"/><description>Clinical Decision Support</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 01 Jul 2020 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Clinical Decision Support</title><link>https://dfki-nlp.github.io/tag/clinical-decision-support/</link></image><item><title>Ex4CDS - Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/dataset/ex4cds/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/ex4cds/</guid><description/></item></channel></rss>
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diff --git a/tag/explainability/index.html b/tag/explainability/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Explainability</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/emnlp2023-feldhus-interrolang/>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</a></h2><div class=article-style>While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such a dialogue system can help …</div></div><div><h2><a href=../../publication/acl-srw2020-harbecke-considering/>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</a></h2><div class=article-style>Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occlusion is a well established method that provides explanations on …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/tag/explainability/index.xml b/tag/explainability/index.xml
index a958664e..53a6d6b1 100644
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Explainability | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/explainability/</link><atom:link href="https://dfki-nlp.github.io/tag/explainability/index.xml" rel="self" type="application/rss+xml"/><description>Explainability</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 06 Dec 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Explainability</title><link>https://dfki-nlp.github.io/tag/explainability/</link></image><item><title>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</title><link>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</link><pubDate>Wed, 06 Dec 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</guid><description/></item><item><title>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</title><link>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</guid><description/></item></channel></rss>
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Explainability | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/explainability/</link><atom:link href="https://dfki-nlp.github.io/tag/explainability/index.xml" rel="self" type="application/rss+xml"/><description>Explainability</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 06 Dec 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Explainability</title><link>https://dfki-nlp.github.io/tag/explainability/</link></image><item><title>InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations</title><link>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</link><pubDate>Wed, 06 Dec 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/emnlp2023-feldhus-interrolang/</guid><description/></item><item><title>Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling</title><link>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</link><pubDate>Fri, 10 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl-srw2020-harbecke-considering/</guid><description/></item></channel></rss>
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diff --git a/tag/explainable-ai/index.html b/tag/explainable-ai/index.html
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--- a/tag/explainable-ai/index.html
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@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Explainable AI</h1></div><div class=universal-wrapper><div><h2><a href=https://github.com/DFKI-NLP/Ex4CDS target=_blank rel=noopener>Ex4CDS - Textual Explanations for Clinical Decision Support</a></h2><div class=article-style>Ex4CDS are explanations (or more precisely justifications) of physicians in the context of clinical decision support. In the course of a larger study, physicians estimated the probability of different clinical outcomes in nephology, namely rejection, graft loss and infections, within the next 90 days. Each estimation had to be justified within a short text - these are our explanations. The explanations were provided in German and have strong similarities to general clinical notes. You can find a description and the data here: https://github.com/DFKI-NLP/Ex4CDS</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/tag/explainable-ai/index.xml b/tag/explainable-ai/index.xml
index 30cbdeea..68b4c57c 100644
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-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Explainable AI | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/explainable-ai/</link><atom:link href="https://dfki-nlp.github.io/tag/explainable-ai/index.xml" rel="self" type="application/rss+xml"/><description>Explainable AI</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 01 Jul 2020 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Explainable AI</title><link>https://dfki-nlp.github.io/tag/explainable-ai/</link></image><item><title>Ex4CDS - Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/dataset/ex4cds/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/ex4cds/</guid><description/></item></channel></rss>
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Explainable AI | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/explainable-ai/</link><atom:link href="https://dfki-nlp.github.io/tag/explainable-ai/index.xml" rel="self" type="application/rss+xml"/><description>Explainable AI</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 01 Jul 2020 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Explainable AI</title><link>https://dfki-nlp.github.io/tag/explainable-ai/</link></image><item><title>Ex4CDS - Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/dataset/ex4cds/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/ex4cds/</guid><description/></item></channel></rss>
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diff --git a/tag/information-extraction/index.html b/tag/information-extraction/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Information Extraction</h1></div><div class=universal-wrapper><div><h2><a href=../../project/data4transparency/>Data4Transparency</a></h2><div class=article-style>According to the World Bank and the UN, some US$1tn is paid in bribes every year. Corrupt financial transactions divert funds from legitimate public services, as well as distort free markets—potentially thwarting economic development—and reduce trust in institutions. The Organized Crime and Corruption Reporting Project (OCCRP) is a global platform for investigative reporting, providing resources to journalists and media centres, enabling cost-effective collaboration between editors and offering tools to secure themselves against threats to independent media. Exposing previously-unknown connections between entities makes it possible for citizens, policymakers, activists and law enforcement agencies to act. As the number of such leaks and publications grows, there is an increasing need for effective, scalable and reproducible methods to discover any anomalies and evidence of malfeasance that might exist within them.</div></div><div><h2><a href=https://www.text2tech.de target=_blank rel=noopener>Text2Tech</a></h2><div class=article-style>The goal of the Text2Tech project is the research and development of automated methods for information extraction from unstructured text sources in order to be able to provide companies with decision-relevant knowledge about technological developments quickly and efficiently. AI-based methods for information extraction (IE) already make it possible to extract selected information, e.g. B. to people, companies and places automatically from text sources. In the Text2Tech project, such approaches are to be further developed in order to extract machine-readable knowledge about technologies, technology categories, companies and their relationships with each other from German and English-language, domain-specific text sources, using the example of the automotive industry. The most important research goals are the modeling and filling of domain-specific knowledge graphs (Knowledge Base Population), the development of methods for cross-lingual proper name recognition and linking (Named Entity Recognition or Entity Linking), relation extraction (Relation Extraction), as well as the development of Model compression methods so that models run efficiently even on small hardware.</div></div><div><h2><a href=https://bifold.berlin/de/ target=_blank rel=noopener>BIFOLD</a></h2><div class=article-style>BIFOLD conducts foundational research in big data management and machine learning, as well as its intersection, to educate future talent, and create high-impact knowledge exchange. The Berlin Institute for the Foundations of Learning and Data (BIFOLD), has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the vibrant Berlin metropolitan area, BIFOLD provides an outstanding scientific environment and numerous collaboration opportunities for national and international researchers. BIFOLD offers a broad range of research topics as well as a platform for interdisciplinary research and knowledge exchange with the sciences and humanities, industry, startups and society. Within BIFOLD, DFKI SLT conducts research in Clinical AI, specifically addressing the task of Pharmacovigilance. Pharmacovigilance is concerned with the assessment and prevention of adverse drug reactions (ADR) in pharmaceutical products. As the level of medication is generally raising all over the world, the potential risk of unwanted side effects, such as ADRs, is constantly increasing. Patients exchange views in their own language as 'experts in their own right,' in social media and disease-specific forums. Our project addresses the detection and extraction of ADR from medical forums and social media across different languages using cross-lingual transfer learning in combination with external knowledge sources.</div></div><div><h2><a href=https://cora4nlp.github.io target=_blank rel=noopener>Cora4NLP</a></h2><div class=article-style>Language is implicit - it omits information. Filling this information gap requires contextual inference, background- and commonsense knowledge, and reasoning over situational context. Language also evolves, i.e., it specializes and changes over time. For example, many different languages and domains exist, new domains arise, and both evolve constantly. Thus, language understanding also requires continuous and efficient adaptation to new languages and domains, and transfer to, and between, both. Current language understanding technology, however, focuses on high resource languages and domains, uses little to no context, and assumes static data, task, and target distributions. The research in Cora4NLP aims to address these challenges. It builds on the expertise and results of the predecessor project DEEPLEE and is carried out jointly between the language technology research departments in Berlin and Saarbrücken.</div></div><div><h2><a href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/bbdcii target=_blank rel=noopener>BBDC2</a></h2><div class=article-style>In order to optimally prepare industry, science and the society in Germany and Europe for the global Big Data trend, highly coordinated activities in research, teaching, and technology transfer regarding the integration of data analysis methods and scalable data processing are required. To achieve this, the Berlin Big Data Center is pursuing the following seven objectives: 1) Pooling expertise in scalable data management, data analytics, and big data application 2) Conducting fundamental research to develop novel and automatically scalable technologies capable of performing 'Deep Analysis' of 'Big Data'. 3) Developing an integrated, declarative, highly scalable open-source system that enables the specification, automatic optimization, parallelization and hardware adaptation, and fault-tolerant, efficient execution of advanced data analysis problems, using varying methods (e.g., drawn from machine learning, linear algebra, statistics and probability theory, computational linguistics, or signal processing), leveraging our work on Apache Flink 4) Transfering technology and know-how to support innovation in companies and startups. 5) Educating data scientists with respect to the five big data dimensions (i.e., applications, economic, legal, social, and technological) via leading educational programs. 6) Empowering people to leverage 'Smart Data', i.e., to discover newfound information based on their massive data sets. 7)Enabling the general public to conduct sound data-driven decision-making.</div></div><div><h2><a href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/deeplee target=_blank rel=noopener>DEEPLEE</a></h2><div class=article-style>The research work in DEEPLEE, which is carried out in the Language Technology research departments in Saabrücken and Berlin, builds on DFKI's expertise in the areas of deep learning (DL) and language technology (LT) and develops it further. They aim for profound improvements of DL approaches in LT by focusing on four central, open research topics: Modularity in DNN architectures, Use of external knowledge, DNNs with explanation functionality, Machine Teaching Strategies for DNNs</div></div><div><h2><a href=https://plass.io target=_blank rel=noopener>PLASS</a></h2><div class=article-style>The aim of the PLASS project is to develop a prototypical B2B platform for AI-based decision support for supply chain management. The focus is on the automatic recognition of decision-relevant information and the acquisition of structured knowledge from global and multilingual text sources. These sources provide a large database for SCM information, especially for the early detection of critical events and risks, but also of opportunities, e.g. through new technologies, at suppliers and supply chains. PLASS enables SMEs and large companies to continuously monitor their suppliers and supply chains, and supports supply chain managers in risk assessment and decision-making.</div></div><div><h2><a href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/sim3s/ target=_blank rel=noopener>SIM3S</a></h2><div class=article-style>In the SIM3S project, data from the BMVI data offerings mCloud and MDM will be linked, refined and jointly analysed with other open data, user-generated content and data from individual modes of transport and other mobility-relevant companies in order to remove barriers and barriers to discrimination in everyday mobility. For the implementation of the project, state-of-the-art technologies and methods from the areas of Big Data Intelligent Analysis of mass data and artificial intelligence, in particular Natural Language Processing (NLP), are used.</div></div><div><h2><a href=https://github.com/DFKI-NLP/cross-ling-adr target=_blank rel=noopener>German Adverse Drug Reaction (ADR) detection in patient-generated content</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection. More info: https://aclanthology.org/2022.lrec-1.388/</div></div><div><h2><a href=https://github.com/DFKI-NLP/sim3s-corpus target=_blank rel=noopener>MobASA Corpus</a></h2><div class=article-style>This repository contains corpus called MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children.
 The data can be used for as a training or test corpus for aspect-oriented sentiment analysis. Moreover, the corpus can benefit building inclusive public transportation systems. You can find the corpus here: https://github.com/DFKI-NLP/sim3s-corpus, and the description of the corpus here: https://aclanthology.org/2022.csrnlp-1.5.pdf</div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../tag/information-extraction/page/2/>&#187;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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diff --git a/tag/information-extraction/index.xml b/tag/information-extraction/index.xml
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diff --git a/tag/information-extraction/page/2/index.html b/tag/information-extraction/page/2/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Information Extraction</h1></div><div class=universal-wrapper><div><h2><a href=https://github.com/DFKI-NLP/MobIE target=_blank rel=noopener>MobIE Corpus</a></h2><div class=article-style>This repository contains the DFKI MobIE Corpus (formerly "DAYSTREAM Corpus"), a dataset of 3,232 German-language documents collected between May 2015 - Apr 2019 that have been annotated with fine-grained geo-entities, such as location-street, location-stop and location-route, as well as standard named entity types (organization, date, number, etc). All location-related entities have been linked to either Open Street Map identifiers or database ids of Deutsche Bahn / Rhein-Main-Verkehrsverbund. The corpus has also been annotated with a set of 7 traffic-related n-ary relations and events, such as Accidents, Traffic jams, and Canceled Routes. It consists of Twitter messages, and traffic reports from e.g. radio stations, police and public transport providers. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, entity linking of these entities, as well as n-ary relation extraction systems. You can find the description of the corpus here: https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/11741/</div></div><div><h2><a href=https://github.com/DFKI-NLP/product-corpus target=_blank rel=noopener>Product Corpus</a></h2><div class=article-style>The Product Corpus is a dataset of 174 English web pages and social media posts annotated for product and company named entities, and the relation CompanyProvidesProduct. The goal is to make extraction of non-standard, B2B products and relations from unstructured text easier and more reliable. The corpus is also annotated for coreference chains of companies and products.</div></div><div><h2><a href=https://github.com/DFKI-NLP/smartdata-corpus target=_blank rel=noopener>SmartData Corpus</a></h2><div class=article-style>The SmartData Corpus is a dataset of 2598 German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as Accidents, Traffic jams, Acquisitions, and Strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems.</div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../../../tag/information-extraction/>&#171;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../../../privacy/>Data Protection Notice</a>
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diff --git a/tag/interpretability/index.html b/tag/interpretability/index.html
index 4164e5cd..a79295c6 100644
--- a/tag/interpretability/index.html
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 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Interpretability</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/acl2023-sarti-inseq/>Inseq: An Interpretability Toolkit for Sequence Generation Models</a></h2><div class=article-style>Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools. In this work, we introduce Inseq, a Python library to …</div></div><div><h2><a href=../../publication/acl2023-feldhus-smv/>Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods</a></h2><div class=article-style>Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more accessible, we formalize the …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/tag/interpretability/index.xml b/tag/interpretability/index.xml
index 2aeddee1..939627a9 100644
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diff --git a/tag/language-understanding/index.html b/tag/language-understanding/index.html
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 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Language Understanding</h1></div><div class=universal-wrapper><div><h2><a href=https://cora4nlp.github.io target=_blank rel=noopener>Cora4NLP</a></h2><div class=article-style>Language is implicit - it omits information. Filling this information gap requires contextual inference, background- and commonsense knowledge, and reasoning over situational context. Language also evolves, i.e., it specializes and changes over time. For example, many different languages and domains exist, new domains arise, and both evolve constantly. Thus, language understanding also requires continuous and efficient adaptation to new languages and domains, and transfer to, and between, both. Current language understanding technology, however, focuses on high resource languages and domains, uses little to no context, and assumes static data, task, and target distributions. The research in Cora4NLP aims to address these challenges. It builds on the expertise and results of the predecessor project DEEPLEE and is carried out jointly between the language technology research departments in Berlin and Saarbrücken.</div></div><div><h2><a href=https://www.dfki.de/en/web/research/projects-and-publications/projects-overview/project/deeplee target=_blank rel=noopener>DEEPLEE</a></h2><div class=article-style>The research work in DEEPLEE, which is carried out in the Language Technology research departments in Saabrücken and Berlin, builds on DFKI's expertise in the areas of deep learning (DL) and language technology (LT) and develops it further. They aim for profound improvements of DL approaches in LT by focusing on four central, open research topics: Modularity in DNN architectures, Use of external knowledge, DNNs with explanation functionality, Machine Teaching Strategies for DNNs</div></div><div><h2><a href=https://github.com/DFKI-NLP/Ex4CDS target=_blank rel=noopener>Ex4CDS - Textual Explanations for Clinical Decision Support</a></h2><div class=article-style>Ex4CDS are explanations (or more precisely justifications) of physicians in the context of clinical decision support. In the course of a larger study, physicians estimated the probability of different clinical outcomes in nephology, namely rejection, graft loss and infections, within the next 90 days. Each estimation had to be justified within a short text - these are our explanations. The explanations were provided in German and have strong similarities to general clinical notes. You can find a description and the data here: https://github.com/DFKI-NLP/Ex4CDS</div></div><div><h2><a href=https://github.com/DFKI-NLP/cross-ling-adr target=_blank rel=noopener>German Adverse Drug Reaction (ADR) detection in patient-generated content</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection. More info: https://aclanthology.org/2022.lrec-1.388/</div></div><div><h2><a href=https://github.com/DFKI-NLP/sim3s-corpus target=_blank rel=noopener>MobASA Corpus</a></h2><div class=article-style>This repository contains corpus called MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children.
 The data can be used for as a training or test corpus for aspect-oriented sentiment analysis. Moreover, the corpus can benefit building inclusive public transportation systems. You can find the corpus here: https://github.com/DFKI-NLP/sim3s-corpus, and the description of the corpus here: https://aclanthology.org/2022.csrnlp-1.5.pdf</div></div><div><h2><a href=https://github.com/DFKI-NLP/MobIE target=_blank rel=noopener>MobIE Corpus</a></h2><div class=article-style>This repository contains the DFKI MobIE Corpus (formerly "DAYSTREAM Corpus"), a dataset of 3,232 German-language documents collected between May 2015 - Apr 2019 that have been annotated with fine-grained geo-entities, such as location-street, location-stop and location-route, as well as standard named entity types (organization, date, number, etc). All location-related entities have been linked to either Open Street Map identifiers or database ids of Deutsche Bahn / Rhein-Main-Verkehrsverbund. The corpus has also been annotated with a set of 7 traffic-related n-ary relations and events, such as Accidents, Traffic jams, and Canceled Routes. It consists of Twitter messages, and traffic reports from e.g. radio stations, police and public transport providers. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, entity linking of these entities, as well as n-ary relation extraction systems. You can find the description of the corpus here: https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/11741/</div></div><div><h2><a href=https://github.com/DFKI-NLP/product-corpus target=_blank rel=noopener>Product Corpus</a></h2><div class=article-style>The Product Corpus is a dataset of 174 English web pages and social media posts annotated for product and company named entities, and the relation CompanyProvidesProduct. The goal is to make extraction of non-standard, B2B products and relations from unstructured text easier and more reliable. The corpus is also annotated for coreference chains of companies and products.</div></div><div><h2><a href=https://github.com/DFKI-NLP/smartdata-corpus target=_blank rel=noopener>SmartData Corpus</a></h2><div class=article-style>The SmartData Corpus is a dataset of 2598 German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as Accidents, Traffic jams, Acquisitions, and Strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems.</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/language-understanding/index.xml b/tag/language-understanding/index.xml
index 5c3e48e6..c3652bad 100644
--- a/tag/language-understanding/index.xml
+++ b/tag/language-understanding/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Language Understanding | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/language-understanding/</link><atom:link href="https://dfki-nlp.github.io/tag/language-understanding/index.xml" rel="self" type="application/rss+xml"/><description>Language Understanding</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Tue, 23 Feb 2021 11:23:02 +0100</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Language Understanding</title><link>https://dfki-nlp.github.io/tag/language-understanding/</link></image><item><title>Cora4NLP</title><link>https://dfki-nlp.github.io/project/cora4nlp/</link><pubDate>Tue, 23 Feb 2021 11:23:02 +0100</pubDate><guid>https://dfki-nlp.github.io/project/cora4nlp/</guid><description/></item><item><title>DEEPLEE</title><link>https://dfki-nlp.github.io/project/deeplee/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/deeplee/</guid><description/></item><item><title>Ex4CDS - Textual Explanations for Clinical Decision Support</title><link>https://dfki-nlp.github.io/dataset/ex4cds/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/ex4cds/</guid><description/></item><item><title>German Adverse Drug Reaction (ADR) detection in patient-generated content</title><link>https://dfki-nlp.github.io/dataset/german-patient-adr/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/german-patient-adr/</guid><description/></item><item><title>MobASA Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</guid><description/></item><item><title>MobIE Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</guid><description/></item><item><title>Product Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-product-corpus/</link><pubDate>Mon, 16 Dec 2019 15:55:08 +0100</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-product-corpus/</guid><description/></item><item><title>SmartData Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-smartdata-corpus/</link><pubDate>Mon, 16 Dec 2019 15:55:08 +0100</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-smartdata-corpus/</guid><description/></item></channel></rss>
\ No newline at end of file
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diff --git a/tag/linguistic-test-suite/index.html b/tag/linguistic-test-suite/index.html
index 125cc1d7..8dcfb887 100644
--- a/tag/linguistic-test-suite/index.html
+++ b/tag/linguistic-test-suite/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>linguistic test suite</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-macketanz-testsuite/>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</a></h2><div class=article-style>This paper presents a fine-grained test suite for the language pair German–English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
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diff --git a/tag/linguistic-test-suite/index.xml b/tag/linguistic-test-suite/index.xml
index e78b4397..889918ee 100644
--- a/tag/linguistic-test-suite/index.xml
+++ b/tag/linguistic-test-suite/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>linguistic test suite | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/linguistic-test-suite/</link><atom:link href="https://dfki-nlp.github.io/tag/linguistic-test-suite/index.xml" rel="self" type="application/rss+xml"/><description>linguistic test suite</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Mon, 22 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>linguistic test suite</title><link>https://dfki-nlp.github.io/tag/linguistic-test-suite/</link></image><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>linguistic test suite | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/linguistic-test-suite/</link><atom:link href="https://dfki-nlp.github.io/tag/linguistic-test-suite/index.xml" rel="self" type="application/rss+xml"/><description>linguistic test suite</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Mon, 22 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>linguistic test suite</title><link>https://dfki-nlp.github.io/tag/linguistic-test-suite/</link></image><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item></channel></rss>
\ No newline at end of file
diff --git a/tag/low-resource-learning/index.html b/tag/low-resource-learning/index.html
index c1685b62..12098bdb 100644
--- a/tag/low-resource-learning/index.html
+++ b/tag/low-resource-learning/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Low-Resource Learning</h1></div><div class=universal-wrapper><div><h2><a href=../../project/data4transparency/>Data4Transparency</a></h2><div class=article-style>According to the World Bank and the UN, some US$1tn is paid in bribes every year. Corrupt financial transactions divert funds from legitimate public services, as well as distort free markets—potentially thwarting economic development—and reduce trust in institutions. The Organized Crime and Corruption Reporting Project (OCCRP) is a global platform for investigative reporting, providing resources to journalists and media centres, enabling cost-effective collaboration between editors and offering tools to secure themselves against threats to independent media. Exposing previously-unknown connections between entities makes it possible for citizens, policymakers, activists and law enforcement agencies to act. As the number of such leaks and publications grows, there is an increasing need for effective, scalable and reproducible methods to discover any anomalies and evidence of malfeasance that might exist within them.</div></div><div><h2><a href=https://www.text2tech.de target=_blank rel=noopener>Text2Tech</a></h2><div class=article-style>The goal of the Text2Tech project is the research and development of automated methods for information extraction from unstructured text sources in order to be able to provide companies with decision-relevant knowledge about technological developments quickly and efficiently. AI-based methods for information extraction (IE) already make it possible to extract selected information, e.g. B. to people, companies and places automatically from text sources. In the Text2Tech project, such approaches are to be further developed in order to extract machine-readable knowledge about technologies, technology categories, companies and their relationships with each other from German and English-language, domain-specific text sources, using the example of the automotive industry. The most important research goals are the modeling and filling of domain-specific knowledge graphs (Knowledge Base Population), the development of methods for cross-lingual proper name recognition and linking (Named Entity Recognition or Entity Linking), relation extraction (Relation Extraction), as well as the development of Model compression methods so that models run efficiently even on small hardware.</div></div><div><h2><a href=https://plass.io target=_blank rel=noopener>PLASS</a></h2><div class=article-style>The aim of the PLASS project is to develop a prototypical B2B platform for AI-based decision support for supply chain management. The focus is on the automatic recognition of decision-relevant information and the acquisition of structured knowledge from global and multilingual text sources. These sources provide a large database for SCM information, especially for the early detection of critical events and risks, but also of opportunities, e.g. through new technologies, at suppliers and supply chains. PLASS enables SMEs and large companies to continuously monitor their suppliers and supply chains, and supports supply chain managers in risk assessment and decision-making.</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/tag/low-resource-learning/index.xml b/tag/low-resource-learning/index.xml
index d80f999d..8f0a8cfc 100644
--- a/tag/low-resource-learning/index.xml
+++ b/tag/low-resource-learning/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Low-Resource Learning | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/low-resource-learning/</link><atom:link href="https://dfki-nlp.github.io/tag/low-resource-learning/index.xml" rel="self" type="application/rss+xml"/><description>Low-Resource Learning</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Sat, 01 Apr 2023 11:16:31 +0100</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Low-Resource Learning</title><link>https://dfki-nlp.github.io/tag/low-resource-learning/</link></image><item><title>Data4Transparency</title><link>https://dfki-nlp.github.io/project/data4transparency/</link><pubDate>Sat, 01 Apr 2023 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/data4transparency/</guid><description/></item><item><title>Text2Tech</title><link>https://dfki-nlp.github.io/project/text2tech/</link><pubDate>Wed, 01 Jun 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/text2tech/</guid><description/></item><item><title>PLASS</title><link>https://dfki-nlp.github.io/project/plass/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/plass/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Low-Resource Learning | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/low-resource-learning/</link><atom:link href="https://dfki-nlp.github.io/tag/low-resource-learning/index.xml" rel="self" type="application/rss+xml"/><description>Low-Resource Learning</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Sat, 01 Apr 2023 11:16:31 +0100</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Low-Resource Learning</title><link>https://dfki-nlp.github.io/tag/low-resource-learning/</link></image><item><title>Data4Transparency</title><link>https://dfki-nlp.github.io/project/data4transparency/</link><pubDate>Sat, 01 Apr 2023 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/data4transparency/</guid><description/></item><item><title>Text2Tech</title><link>https://dfki-nlp.github.io/project/text2tech/</link><pubDate>Wed, 01 Jun 2022 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/text2tech/</guid><description/></item><item><title>PLASS</title><link>https://dfki-nlp.github.io/project/plass/</link><pubDate>Tue, 23 Feb 2021 11:16:31 +0100</pubDate><guid>https://dfki-nlp.github.io/project/plass/</guid><description/></item></channel></rss>
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diff --git a/tag/machine-generated-text/index.html b/tag/machine-generated-text/index.html
index 23073674..16f298ed 100644
--- a/tag/machine-generated-text/index.html
+++ b/tag/machine-generated-text/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>machine-generated text</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/acl2022-humeval-macketanz-perceptual/>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</a></h2><div class=article-style>The quality of machine-generated text is a complex construct consisting of various aspects and dimensions. We present a study that aims to uncover relevant perceptual quality dimensions for one type of machine-generated text, that is, Machine …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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diff --git a/tag/machine-generated-text/index.xml b/tag/machine-generated-text/index.xml
index fb842ac5..a4d836bd 100644
--- a/tag/machine-generated-text/index.xml
+++ b/tag/machine-generated-text/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>machine-generated text | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/machine-generated-text/</link><atom:link href="https://dfki-nlp.github.io/tag/machine-generated-text/index.xml" rel="self" type="application/rss+xml"/><description>machine-generated text</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Sun, 12 Jun 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>machine-generated text</title><link>https://dfki-nlp.github.io/tag/machine-generated-text/</link></image><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>machine-generated text | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/machine-generated-text/</link><atom:link href="https://dfki-nlp.github.io/tag/machine-generated-text/index.xml" rel="self" type="application/rss+xml"/><description>machine-generated text</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Sun, 12 Jun 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>machine-generated text</title><link>https://dfki-nlp.github.io/tag/machine-generated-text/</link></image><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item></channel></rss>
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diff --git a/tag/machine-translation/index.html b/tag/machine-translation/index.html
index d4d43a82..352e3775 100644
--- a/tag/machine-translation/index.html
+++ b/tag/machine-translation/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Machine Translation</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/vandeghinste-2023-ele-sign/>European Language Equality, Report on Europe's Sign Languages</a></h2><div class=article-style>This report on Europe's Sign Languages is part of a series of language deliverables developed within the framework of the European Language Equality (ELE) project. The series seeks to not only delineate the current state of affairs for each European …</div></div><div><h2><a href=../../publication/acl2023-zhu-sign/>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</a></h2><div class=article-style>State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language (SL) glosses. In our experiments, we improve the performance of the transformer-based models via (1) data …</div></div><div><h2><a href=../../publication/lrec2022-macketanz-testsuite/>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</a></h2><div class=article-style>This paper presents a fine-grained test suite for the language pair German–English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
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-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
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diff --git a/tag/machine-translation/index.xml b/tag/machine-translation/index.xml
index 0df134a1..b1af7dd4 100644
--- a/tag/machine-translation/index.xml
+++ b/tag/machine-translation/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Translation | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/machine-translation/</link><atom:link href="https://dfki-nlp.github.io/tag/machine-translation/index.xml" rel="self" type="application/rss+xml"/><description>Machine Translation</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Fri, 21 Jul 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Machine Translation</title><link>https://dfki-nlp.github.io/tag/machine-translation/</link></image><item><title>European Language Equality, Report on Europe's Sign Languages</title><link>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</link><pubDate>Fri, 21 Jul 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</guid><description/></item><item><title>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</title><link>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</guid><description/></item><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Translation | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/machine-translation/</link><atom:link href="https://dfki-nlp.github.io/tag/machine-translation/index.xml" rel="self" type="application/rss+xml"/><description>Machine Translation</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 21 Jul 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Machine Translation</title><link>https://dfki-nlp.github.io/tag/machine-translation/</link></image><item><title>European Language Equality, Report on Europe's Sign Languages</title><link>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</link><pubDate>Fri, 21 Jul 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/vandeghinste-2023-ele-sign/</guid><description/></item><item><title>Neural Machine Translation Methods for Translating Text to Sign Language Glosses</title><link>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</link><pubDate>Thu, 15 Jun 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/publication/acl2023-zhu-sign/</guid><description/></item><item><title>A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output</title><link>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</link><pubDate>Mon, 22 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-macketanz-testsuite/</guid><description/></item></channel></rss>
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diff --git a/tag/mobility/index.html b/tag/mobility/index.html
index 15a113e5..e41a91fd 100644
--- a/tag/mobility/index.html
+++ b/tag/mobility/index.html
@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Mobility</h1></div><div class=universal-wrapper><div><h2><a href=https://github.com/DFKI-NLP/sim3s-corpus target=_blank rel=noopener>MobASA Corpus</a></h2><div class=article-style>This repository contains corpus called MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children.
 The data can be used for as a training or test corpus for aspect-oriented sentiment analysis. Moreover, the corpus can benefit building inclusive public transportation systems. You can find the corpus here: https://github.com/DFKI-NLP/sim3s-corpus, and the description of the corpus here: https://aclanthology.org/2022.csrnlp-1.5.pdf</div></div><div><h2><a href=https://github.com/DFKI-NLP/MobIE target=_blank rel=noopener>MobIE Corpus</a></h2><div class=article-style>This repository contains the DFKI MobIE Corpus (formerly "DAYSTREAM Corpus"), a dataset of 3,232 German-language documents collected between May 2015 - Apr 2019 that have been annotated with fine-grained geo-entities, such as location-street, location-stop and location-route, as well as standard named entity types (organization, date, number, etc). All location-related entities have been linked to either Open Street Map identifiers or database ids of Deutsche Bahn / Rhein-Main-Verkehrsverbund. The corpus has also been annotated with a set of 7 traffic-related n-ary relations and events, such as Accidents, Traffic jams, and Canceled Routes. It consists of Twitter messages, and traffic reports from e.g. radio stations, police and public transport providers. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, entity linking of these entities, as well as n-ary relation extraction systems. You can find the description of the corpus here: https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/11741/</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
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diff --git a/tag/mobility/index.xml b/tag/mobility/index.xml
index f7241759..6418f335 100644
--- a/tag/mobility/index.xml
+++ b/tag/mobility/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mobility | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/mobility/</link><atom:link href="https://dfki-nlp.github.io/tag/mobility/index.xml" rel="self" type="application/rss+xml"/><description>Mobility</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 01 Jul 2020 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Mobility</title><link>https://dfki-nlp.github.io/tag/mobility/</link></image><item><title>MobASA Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</guid><description/></item><item><title>MobIE Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mobility | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/mobility/</link><atom:link href="https://dfki-nlp.github.io/tag/mobility/index.xml" rel="self" type="application/rss+xml"/><description>Mobility</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 01 Jul 2020 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Mobility</title><link>https://dfki-nlp.github.io/tag/mobility/</link></image><item><title>MobASA Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</guid><description/></item><item><title>MobIE Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobie-corpus/</guid><description/></item></channel></rss>
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diff --git a/tag/multilinguality/index.html b/tag/multilinguality/index.html
index 45bebc5a..7d984b67 100644
--- a/tag/multilinguality/index.html
+++ b/tag/multilinguality/index.html
@@ -6,7 +6,7 @@
 The dataset will be released via the LDC (link will follow).
 Please see [our ACL paper](https://arxiv.org/abs/2305.04582) for full details. You can find the Github repo containing the translation and experiment code here [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED).</div></div><div><h2><a href=https://github.com/DFKI-NLP/cross-ling-adr target=_blank rel=noopener>German Adverse Drug Reaction (ADR) detection in patient-generated content</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection. More info: https://aclanthology.org/2022.lrec-1.388/</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
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diff --git a/tag/multilinguality/index.xml b/tag/multilinguality/index.xml
index 7b27f9ff..06597352 100644
--- a/tag/multilinguality/index.xml
+++ b/tag/multilinguality/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multilinguality | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/multilinguality/</link><atom:link href="https://dfki-nlp.github.io/tag/multilinguality/index.xml" rel="self" type="application/rss+xml"/><description>Multilinguality</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Multilinguality</title><link>https://dfki-nlp.github.io/tag/multilinguality/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item><item><title>German Adverse Drug Reaction (ADR) detection in patient-generated content</title><link>https://dfki-nlp.github.io/dataset/german-patient-adr/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/german-patient-adr/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multilinguality | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/multilinguality/</link><atom:link href="https://dfki-nlp.github.io/tag/multilinguality/index.xml" rel="self" type="application/rss+xml"/><description>Multilinguality</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Multilinguality</title><link>https://dfki-nlp.github.io/tag/multilinguality/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item><item><title>German Adverse Drug Reaction (ADR) detection in patient-generated content</title><link>https://dfki-nlp.github.io/dataset/german-patient-adr/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/german-patient-adr/</guid><description/></item></channel></rss>
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diff --git a/tag/pharmacovigilance/index.html b/tag/pharmacovigilance/index.html
index 2f249222..05b03332 100644
--- a/tag/pharmacovigilance/index.html
+++ b/tag/pharmacovigilance/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>pharmacovigilance</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-raithel-cross-adr/>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/pharmacovigilance/index.xml b/tag/pharmacovigilance/index.xml
index 618b8294..a834e748 100644
--- a/tag/pharmacovigilance/index.xml
+++ b/tag/pharmacovigilance/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>pharmacovigilance | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/pharmacovigilance/</link><atom:link href="https://dfki-nlp.github.io/tag/pharmacovigilance/index.xml" rel="self" type="application/rss+xml"/><description>pharmacovigilance</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>pharmacovigilance</title><link>https://dfki-nlp.github.io/tag/pharmacovigilance/</link></image><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>pharmacovigilance | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/pharmacovigilance/</link><atom:link href="https://dfki-nlp.github.io/tag/pharmacovigilance/index.xml" rel="self" type="application/rss+xml"/><description>pharmacovigilance</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>pharmacovigilance</title><link>https://dfki-nlp.github.io/tag/pharmacovigilance/</link></image><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item></channel></rss>
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diff --git a/tag/relation-extraction/index.html b/tag/relation-extraction/index.html
index fe4c6d4e..52432fad 100644
--- a/tag/relation-extraction/index.html
+++ b/tag/relation-extraction/index.html
@@ -6,7 +6,7 @@
 The dataset will be released via the LDC (link will follow).
 Please see [our ACL paper](https://arxiv.org/abs/2305.04582) for full details. You can find the Github repo containing the translation and experiment code here [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED).</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/relation-extraction/index.xml b/tag/relation-extraction/index.xml
index ea78305b..cf56e4e4 100644
--- a/tag/relation-extraction/index.xml
+++ b/tag/relation-extraction/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Relation Extraction | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/relation-extraction/</link><atom:link href="https://dfki-nlp.github.io/tag/relation-extraction/index.xml" rel="self" type="application/rss+xml"/><description>Relation Extraction</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Relation Extraction</title><link>https://dfki-nlp.github.io/tag/relation-extraction/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Relation Extraction | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/relation-extraction/</link><atom:link href="https://dfki-nlp.github.io/tag/relation-extraction/index.xml" rel="self" type="application/rss+xml"/><description>Relation Extraction</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Relation Extraction</title><link>https://dfki-nlp.github.io/tag/relation-extraction/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item></channel></rss>
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diff --git a/tag/semantic-differential/index.html b/tag/semantic-differential/index.html
index 68ac4f8c..ab38f656 100644
--- a/tag/semantic-differential/index.html
+++ b/tag/semantic-differential/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>semantic differential</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/acl2022-humeval-macketanz-perceptual/>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</a></h2><div class=article-style>The quality of machine-generated text is a complex construct consisting of various aspects and dimensions. We present a study that aims to uncover relevant perceptual quality dimensions for one type of machine-generated text, that is, Machine …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/semantic-differential/index.xml b/tag/semantic-differential/index.xml
index eb887c84..b8a0bdc9 100644
--- a/tag/semantic-differential/index.xml
+++ b/tag/semantic-differential/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>semantic differential | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/semantic-differential/</link><atom:link href="https://dfki-nlp.github.io/tag/semantic-differential/index.xml" rel="self" type="application/rss+xml"/><description>semantic differential</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Sun, 12 Jun 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>semantic differential</title><link>https://dfki-nlp.github.io/tag/semantic-differential/</link></image><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>semantic differential | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/semantic-differential/</link><atom:link href="https://dfki-nlp.github.io/tag/semantic-differential/index.xml" rel="self" type="application/rss+xml"/><description>semantic differential</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Sun, 12 Jun 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>semantic differential</title><link>https://dfki-nlp.github.io/tag/semantic-differential/</link></image><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item></channel></rss>
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diff --git a/tag/sentiment-analysis/index.html b/tag/sentiment-analysis/index.html
index c8b64dcb..8de19e56 100644
--- a/tag/sentiment-analysis/index.html
+++ b/tag/sentiment-analysis/index.html
@@ -4,7 +4,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Sentiment Analysis</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-gabryszak/>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</a></h2><div class=article-style>In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance …</div></div><div><h2><a href=https://github.com/DFKI-NLP/sim3s-corpus target=_blank rel=noopener>MobASA Corpus</a></h2><div class=article-style>This repository contains corpus called MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children.
 The data can be used for as a training or test corpus for aspect-oriented sentiment analysis. Moreover, the corpus can benefit building inclusive public transportation systems. You can find the corpus here: https://github.com/DFKI-NLP/sim3s-corpus, and the description of the corpus here: https://aclanthology.org/2022.csrnlp-1.5.pdf</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
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diff --git a/tag/sentiment-analysis/index.xml b/tag/sentiment-analysis/index.xml
index 0e16cf4b..f4f9becc 100644
--- a/tag/sentiment-analysis/index.xml
+++ b/tag/sentiment-analysis/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sentiment Analysis | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/sentiment-analysis/</link><atom:link href="https://dfki-nlp.github.io/tag/sentiment-analysis/index.xml" rel="self" type="application/rss+xml"/><description>Sentiment Analysis</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Sentiment Analysis</title><link>https://dfki-nlp.github.io/tag/sentiment-analysis/</link></image><item><title>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</guid><description/></item><item><title>MobASA Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sentiment Analysis | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/sentiment-analysis/</link><atom:link href="https://dfki-nlp.github.io/tag/sentiment-analysis/index.xml" rel="self" type="application/rss+xml"/><description>Sentiment Analysis</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Sentiment Analysis</title><link>https://dfki-nlp.github.io/tag/sentiment-analysis/</link></image><item><title>MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain</title><link>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-gabryszak/</guid><description/></item><item><title>MobASA Corpus</title><link>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</link><pubDate>Wed, 01 Jul 2020 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/dfki-mobasa-corpus/</guid><description/></item></channel></rss>
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diff --git a/tag/summarization/index.html b/tag/summarization/index.html
index 2a9026f9..d684f17d 100644
--- a/tag/summarization/index.html
+++ b/tag/summarization/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>summarization</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-calizzano/>Generating Extended and Multilingual Summaries with Pre-trained Transformers</a></h2><div class=article-style>Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/summarization/index.xml b/tag/summarization/index.xml
index cefc446b..a6b8f789 100644
--- a/tag/summarization/index.xml
+++ b/tag/summarization/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>summarization | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/summarization/</link><atom:link href="https://dfki-nlp.github.io/tag/summarization/index.xml" rel="self" type="application/rss+xml"/><description>summarization</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>summarization</title><link>https://dfki-nlp.github.io/tag/summarization/</link></image><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>summarization | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/summarization/</link><atom:link href="https://dfki-nlp.github.io/tag/summarization/index.xml" rel="self" type="application/rss+xml"/><description>summarization</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>summarization</title><link>https://dfki-nlp.github.io/tag/summarization/</link></image><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item></channel></rss>
\ No newline at end of file
diff --git a/tag/text-classification/index.html b/tag/text-classification/index.html
index 35e48240..0bcbd808 100644
--- a/tag/text-classification/index.html
+++ b/tag/text-classification/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>text classification</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-raithel-cross-adr/>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</a></h2><div class=article-style>In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/text-classification/index.xml b/tag/text-classification/index.xml
index 08d4d841..056597f2 100644
--- a/tag/text-classification/index.xml
+++ b/tag/text-classification/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>text classification | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/text-classification/</link><atom:link href="https://dfki-nlp.github.io/tag/text-classification/index.xml" rel="self" type="application/rss+xml"/><description>text classification</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>text classification</title><link>https://dfki-nlp.github.io/tag/text-classification/</link></image><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>text classification | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/text-classification/</link><atom:link href="https://dfki-nlp.github.io/tag/text-classification/index.xml" rel="self" type="application/rss+xml"/><description>text classification</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>text classification</title><link>https://dfki-nlp.github.io/tag/text-classification/</link></image><item><title>Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective</title><link>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-raithel-cross-adr/</guid><description/></item></channel></rss>
\ No newline at end of file
diff --git a/tag/text-quality/index.html b/tag/text-quality/index.html
index ec476112..a435d9c4 100644
--- a/tag/text-quality/index.html
+++ b/tag/text-quality/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>text quality</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/acl2022-humeval-macketanz-perceptual/>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</a></h2><div class=article-style>The quality of machine-generated text is a complex construct consisting of various aspects and dimensions. We present a study that aims to uncover relevant perceptual quality dimensions for one type of machine-generated text, that is, Machine …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
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diff --git a/tag/text-quality/index.xml b/tag/text-quality/index.xml
index cc31baa5..f0111318 100644
--- a/tag/text-quality/index.xml
+++ b/tag/text-quality/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>text quality | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/text-quality/</link><atom:link href="https://dfki-nlp.github.io/tag/text-quality/index.xml" rel="self" type="application/rss+xml"/><description>text quality</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Sun, 12 Jun 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>text quality</title><link>https://dfki-nlp.github.io/tag/text-quality/</link></image><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>text quality | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/text-quality/</link><atom:link href="https://dfki-nlp.github.io/tag/text-quality/index.xml" rel="self" type="application/rss+xml"/><description>text quality</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Sun, 12 Jun 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>text quality</title><link>https://dfki-nlp.github.io/tag/text-quality/</link></image><item><title>Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation</title><link>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</link><pubDate>Sun, 12 Jun 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/acl2022-humeval-macketanz-perceptual/</guid><description/></item></channel></rss>
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diff --git a/tag/transfer-learning/index.html b/tag/transfer-learning/index.html
index 1972684c..224d4cc2 100644
--- a/tag/transfer-learning/index.html
+++ b/tag/transfer-learning/index.html
@@ -6,7 +6,7 @@
 The dataset will be released via the LDC (link will follow).
 Please see [our ACL paper](https://arxiv.org/abs/2305.04582) for full details. You can find the Github repo containing the translation and experiment code here [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED).</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/transfer-learning/index.xml b/tag/transfer-learning/index.xml
index 9d6efc6c..2842b344 100644
--- a/tag/transfer-learning/index.xml
+++ b/tag/transfer-learning/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Transfer Learning | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/transfer-learning/</link><atom:link href="https://dfki-nlp.github.io/tag/transfer-learning/index.xml" rel="self" type="application/rss+xml"/><description>Transfer Learning</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Transfer Learning</title><link>https://dfki-nlp.github.io/tag/transfer-learning/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Transfer Learning | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/transfer-learning/</link><atom:link href="https://dfki-nlp.github.io/tag/transfer-learning/index.xml" rel="self" type="application/rss+xml"/><description>Transfer Learning</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 24 May 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Transfer Learning</title><link>https://dfki-nlp.github.io/tag/transfer-learning/</link></image><item><title>The MultiTACRED dataset</title><link>https://dfki-nlp.github.io/dataset/multitacred/</link><pubDate>Wed, 24 May 2023 00:00:00 +0000</pubDate><guid>https://dfki-nlp.github.io/dataset/multitacred/</guid><description/></item></channel></rss>
\ No newline at end of file
diff --git a/tag/wikinews/index.html b/tag/wikinews/index.html
index e38bacc5..ba2e18c7 100644
--- a/tag/wikinews/index.html
+++ b/tag/wikinews/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>wikinews</h1></div><div class=universal-wrapper><div><h2><a href=../../publication/lrec2022-calizzano/>Generating Extended and Multilingual Summaries with Pre-trained Transformers</a></h2><div class=article-style>Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisation tailored for extended …</div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
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 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/tag/wikinews/index.xml b/tag/wikinews/index.xml
index e2a2c095..645dc8c0 100644
--- a/tag/wikinews/index.xml
+++ b/tag/wikinews/index.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>wikinews | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/wikinews/</link><atom:link href="https://dfki-nlp.github.io/tag/wikinews/index.xml" rel="self" type="application/rss+xml"/><description>wikinews</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2023</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>wikinews</title><link>https://dfki-nlp.github.io/tag/wikinews/</link></image><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item></channel></rss>
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>wikinews | DFKI-NLP</title><link>https://dfki-nlp.github.io/tag/wikinews/</link><atom:link href="https://dfki-nlp.github.io/tag/wikinews/index.xml" rel="self" type="application/rss+xml"/><description>wikinews</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Fri, 12 Aug 2022 15:12:24 +0200</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>wikinews</title><link>https://dfki-nlp.github.io/tag/wikinews/</link></image><item><title>Generating Extended and Multilingual Summaries with Pre-trained Transformers</title><link>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</link><pubDate>Fri, 12 Aug 2022 15:12:24 +0200</pubDate><guid>https://dfki-nlp.github.io/publication/lrec2022-calizzano/</guid><description/></item></channel></rss>
\ No newline at end of file
diff --git a/tags/index.html b/tags/index.html
index cabe0408..a8b4cd4c 100644
--- a/tags/index.html
+++ b/tags/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Tags</h1></div><div class=universal-wrapper><div><h2><a href=../tag/explainability/>Explainability</a></h2><div class=article-style></div></div><div><h2><a href=../tag/machine-translation/>Machine Translation</a></h2><div class=article-style></div></div><div><h2><a href=../tag/interpretability/>Interpretability</a></h2><div class=article-style></div></div><div><h2><a href=../tag/multilinguality/>Multilinguality</a></h2><div class=article-style></div></div><div><h2><a href=../tag/relation-extraction/>Relation Extraction</a></h2><div class=article-style></div></div><div><h2><a href=../tag/transfer-learning/>Transfer Learning</a></h2><div class=article-style></div></div><div><h2><a href=../tag/information-extraction/>Information Extraction</a></h2><div class=article-style></div></div><div><h2><a href=../tag/low-resource-learning/>Low-Resource Learning</a></h2><div class=article-style></div></div><div><h2><a href=../tag/linguistic-test-suite/>linguistic test suite</a></h2><div class=article-style></div></div><div><h2><a href=../tag/adverse-drug-reactions/>adverse drug reactions</a></h2><div class=article-style></div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../tags/page/2/>&#187;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
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diff --git a/tags/index.xml b/tags/index.xml
index cae078d1..c0492fca 100644
--- a/tags/index.xml
+++ b/tags/index.xml
@@ -1 +1 @@
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\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tags | DFKI-NLP</title><link>https://dfki-nlp.github.io/tags/</link><atom:link href="https://dfki-nlp.github.io/tags/index.xml" rel="self" type="application/rss+xml"/><description>Tags</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><copyright>© DFKI-NLP 2019-2024</copyright><lastBuildDate>Wed, 06 Dec 2023 00:00:00 +0000</lastBuildDate><image><url>https://dfki-nlp.github.io/images/icon_hu15d4cd24fad375030c8e4d8f45deb950_164099_512x512_fill_lanczos_center_2.png</url><title>Tags</title><link>https://dfki-nlp.github.io/tags/</link></image></channel></rss>
\ No newline at end of file
diff --git a/tags/page/2/index.html b/tags/page/2/index.html
index f6264aaf..2e09455a 100644
--- a/tags/page/2/index.html
+++ b/tags/page/2/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Tags</h1></div><div class=universal-wrapper><div><h2><a href=../../../tag/barrier-free-travel/>barrier-free travel</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/pharmacovigilance/>pharmacovigilance</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/sentiment-analysis/>Sentiment Analysis</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/summarization/>summarization</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/text-classification/>text classification</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/wikinews/>wikinews</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/machine-generated-text/>machine-generated text</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/semantic-differential/>semantic differential</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/text-quality/>text quality</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/language-understanding/>Language Understanding</a></h2><div class=article-style></div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../../tags/>&#171;</a></li><li class=page-item><a class=page-link href=../../../tags/page/3/>&#187;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
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diff --git a/tags/page/3/index.html b/tags/page/3/index.html
index 63d5ac3a..37383639 100644
--- a/tags/page/3/index.html
+++ b/tags/page/3/index.html
@@ -3,7 +3,7 @@
 <a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a>
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class="universal-wrapper pt-3"><h1>Tags</h1></div><div class=universal-wrapper><div><h2><a href=../../../tag/clinical-decision-support/>Clinical Decision Support</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/explainable-ai/>Explainable AI</a></h2><div class=article-style></div></div><div><h2><a href=../../../tag/mobility/>Mobility</a></h2><div class=article-style></div></div><nav><ul class="pagination justify-content-center"><li class=page-item><a class=page-link href=../../../tags/page/2/>&#171;</a></li></ul></nav></div></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../../../privacy/>Data Protection Notice</a>
 &#183;
-<a href=../../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2023</p><p class=powered-by>Published with
+<a href=../../../terms/>Legal Information</a></p><p class=powered-by>© DFKI-NLP 2019-2024</p><p class=powered-by>Published with
 <a href="https://wowchemy.com/?utm_campaign=poweredby" target=_blank rel=noopener>Wowchemy</a> —
 the free, <a href=https://github.com/wowchemy/wowchemy-hugo-modules target=_blank rel=noopener>open source</a> website builder that empowers creators.</p></footer></div></div><div id=modal class="modal fade" role=dialog><div class=modal-dialog><div class=modal-content><div class=modal-header><h5 class=modal-title>Cite</h5><button type=button class=close data-dismiss=modal aria-label=Close>
 <span aria-hidden=true>&#215;</span></button></div><div class=modal-body><pre><code class="tex hljs"></code></pre></div><div class=modal-footer><a class="btn btn-outline-primary my-1 js-copy-cite" href=# target=_blank><i class="fas fa-copy"></i>Copy</a>
diff --git a/terms/index.html b/terms/index.html
index 0f0765b9..7dcc770a 100644
--- a/terms/index.html
+++ b/terms/index.html
@@ -8,7 +8,7 @@
 <a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><article class=article><div class="article-container pt-3"><h1>Legal Information</h1><div class=article-metadata></div></div><div class=article-container><div class=article-style><h2 id=responsible-service-provider>Responsible service provider</h2><p>Responsible for the content of the domain dfki-nlp.github.io from the point of view of § 5 TMG:</p><p>Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)
 Management:<br>Prof. Dr. Antonio Krüger<br>Helmut Ditzer<br>Trippstadter Str. 122<br>67663 Kaiserslautern<br>Germany<br>Phone: +49 631 20575 0<br>Fax: +49 631 20575 5030<br>Email: <a href=mailto:info@dfki.de>info@dfki.de</a></p><p>Register Court: Amtsgericht Kaiserslautern<br>Register Number: HRB 2313</p><p>ID-Number: DE 148 646 973</p><p>The person responsible for the editorial content of the domain cora4nlp.github.io of the German Research Center for Artificial Intelligence GmbH within the meaning of § 18 para. 2 MStV is:</p><p>Dr. Leonhard Hennig, Senior Researcher<br>DFKI Lab Berlin<br>Alt-Moabit 91c<br>D-10559 Berlin<br>Tel: +49 (0)30 / 238 95-0<br>Email: <a href=mailto:leonhard.hennig@dfki.de>leonhard.hennig@dfki.de</a><br>Website URL: <a href=http://www.dfki.de>www.dfki.de</a></p><h2 id=legal-notice-concerning-liability-for-proprietary-content>Legal notice concerning liability for proprietary content</h2><p>As a content provider in accordance with Section 7 (1) of the German Telemedia Act (Telemediengesetz), the Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI) is responsible for its own content that is used pursuant to the general laws. The DFKI endeavors to ensure that the information provided on this website is accurate and current. Nevertheless, errors and uncertainties cannot be entirely ruled out. For this reason, the DFKI undertakes no liability for ensuring that the provided information is current, accurate or complete, and is not responsible for its quality. The DFKI is not liable for material or immaterial damages caused directly or indirectly by the use or non-use of the offered information, or by the use of erroneous and incomplete information, unless willful or grossly negligent fault can be demonstrated. This also applies with respect to software or data provided for download. The DFKI reserves the right to modify, expand or delete parts of the website or the entire website without separate announcement, or to cease publication temporarily or definitively.</p><h2 id=legal-notices-for-third-party-content-and-references-to-external-websites>Legal notices for third party content and references to external websites</h2><p>As a service provider, we are responsible for our content on these pages in accordance with general law (§ 7 (1) TMG). According to § 8 – 10 TMG, as a service provider, we are not obliged to monior third-party information that is transmitted or stored, or to investigate circumstances that indicate illegal activity. Obligations to remove or block the use of information according to general laws remain unaffected. However, liability in this regard is only possible form the point in time at which we become aware of a specific legal violation. If we become aware of legal violations, we will remove this content immediately. Cross- references (“links”) to the content providers are to be distinguished from own content. Our offer contains links to external third party website. The respective provider is always responsible for the content of the linked external pages. We cannot accept any liability for the content for the content of the linked pages. After this external content was checked by the DFKI when the link was recenty set, it was checked whether there were any preliminary legal violations. At the time oft he review, no legal violations were apparent. However, it cannot be ruled out that the content may be changed afterwards by teh resprective providers. A permanent control oft he content of the linked pages is not reasonable without ecidence of a violation of the law. The DFKI does not constantly check the content to which it refers in ist offer for changes that could justify a new responsibility. If you are oft he option that the linked external pages violate applicable law or have otherwie inappropriate content, please inform us directly: <a href=mailto:info@dfki.de>info@dfki.de</a>. Should the DFKI discover or receive a message that an external offer to whicht is has linked triggers civil or criminal liability, the DFKI will remove the link to this offer.</p><h2 id=legal-notice-concerning-copyright>Legal notice concerning copyright</h2><p>The layout of the homepage, the graphics used and other content on the DFKI website are protected by copyright. The reproduction, processing, distribution and any type of use outside the boundaries of copyright law require the written approval of the DFKI (in writing). Insofar as any content on this page was not prepared by the DFKI, the copyrights of third parties will be observed. If you become aware of a copyright breach nevertheless, please inform us accordingly. Upon becoming aware of relevant legal breaches, the DFKI will remove such content immediately.</p></div></div></article></div><div class=page-footer><div class=container><footer class=site-footer><p class=powered-by><a href=../privacy/>Data Protection Notice</a>
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