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<h2>Publications @ INK</h2> <br><br><br>
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/" target="_blank">INK Lab @ Github</a></h4>
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/TriggerNER" target="_blank">TriggerNER: </a> Learning with Entity Triggers as Explanations for Named Entity Recognition</h4>
An implementation of learning with entity trigger as explanations for the Named Entity Recognition (NER) task (ACL 2020).
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="http://inklab.usc.edu/leanlife/" target="_blank">Lean-Life: </a> A Label-Efficient Annotation Framework Towards Learning from Explanation</h4>
A web based framework for obtaining human explanation annotations (ACL 2020, System Demo).
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/NExT" target="_blank">NExT: </a> Learning from Explanations with Neural Module Execution Tree</h4>
An implementation of the proposed Neural Module Execution Tree frameworks for learning from explanations (ICLR 2020).
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/NERO" target="_blank">NERO: </a> A Neural Rule Grounding Framework for Label-Efficient Relation Extraction</h4>
An implementation of the proposed neural rule grounding framework (WWW 2020).
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/hierarchical-explanation" target="_blank">HiExpl: </a> Hierarchical explanations of neural sequence model predictions</h4>
Pytorch implementation of "Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models".
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/KagNet" target="_blank">KagNet: </a> Knowledge-Aware Graph Networks for Commonsense Reasoning</h4>
An implementation of the proposed KagNet model for commonsense reasoning (EMNLP-IJCNLP 2019).
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<img class="3u 12u$(xsmall)" style="border:1px solid #ddd; padding: 0;" src="https://github.com/shanzhenren/CPL/raw/master/emnlp-gif.gif?raw=true"/>
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/CPL" target="_blank">CPL: </a> Collaborative Policy Learning for Open Knowledge Graph Reasoning</h4>
TensorFlow implementation of EMNLP 2019 paper Collaborative Policy Learning for Open Knowledge Graph Reasoning. Moving to PyTorch.
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/RE-Net" target="_blank">Recurrent Event Network:</a> Global Structure Inference over Temporal Knowledge Graph</h4>
Pytorch implementation for Recurrent Event Network: Global Structure Inference over Temporal Knowledge Graph (ICLR-RLGM 2019), which is an autoregressive model to infer graph structures at unobserved times on temporal knowledge graphs (extrapolation problem).
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<img class="3u 12u$(xsmall)" style="border:1px solid #ddd; padding: 0;" src="images/paper_gists/alpeca.gif"/>
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="AlpacaTag/index.html" target="_blank">AlpacaTag:</a> Active Learning-based Tagging Framework.</h4>
AlpacaTag is an open-source web-based data annotation framework for sequence tagging tasks, such as named-entity recognition (NER).
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<br> <br>
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<img class="3u 12u$(xsmall)" style="border:1px solid #ddd; padding: 0;" src="images/paper_gists/cotype.jpg"/>
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<h4 style="margin: 0em 0em 0em 0em;"><a style="text-decoration: underline;" href="https://github.com/INK-USC/DS-RelationExtraction" target="_blank">DS-RelationExtraction:</a> Distantly-supervised Relation Extraction with Knowledge Bases</h4>
This repository puts together recent models and data sets for sentence-level relation extraction using knowledge bases (i.e., distant supervision). In particular, it contains the source code for WWW'17 paper CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases.
Task: Given a text corpus with entity mentions detected and heuristically labeled using distant supervision, the task aims to identify relation types/labels between a pair of entity mentions based on the sentence context where they co-occur.
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<h4 style="margin: 0em 0em 0em 0em;"> <a style="text-decoration: underline;" href="https://github.com/INK-USC/ReQuest" target="_blank">ReQuest:</a> Relation Extraction with Question-Answer Pairs.</h4>
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