Title: MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
Abstract: https://arxiv.org/abs/2007.15207
Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on a heavily curated, language-independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state-of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages
Homepage: https://github.com/apple/ml-mkqa
@article{ozturk2021ironytr,
title={IronyTR: Irony Detection in Turkish Informal Texts},
author={Ozturk, Asli Umay and Cemek, Yesim and Karagoz, Pinar},
journal={International Journal of Intelligent Information Technologies (IJIIT)},
volume={17},
number={4},
pages={1--18},
year={2021},
publisher={IGI Global}
}
@inproceedings{cemek2020investigating,
title={Investigating the Neural Models for Irony Detection on Turkish Informal Texts},
author={Cemek, Ye{\c{s}}im and Cidecio, Cenk and {\"O}zt{\"u}rk, Asl{\i} Umay and {\c{C}}ekinel, Recep F{\i}rat and Karag{\"o}z, P{\i}nar},
booktitle={2020 28th Signal Processing and Communications Applications Conference (SIU)},
pages={1--4},
year={2020},
organization={IEEE}
}