Jean Lee


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K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
Jean Lee | Taejun Lim | Heejun Lee | Bogeun Jo | Yangsok Kim | Heegeun Yoon | Soyeon Caren Han
Proceedings of the 29th International Conference on Computational Linguistics

Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.


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CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Henry Weld | Guanghao Huang | Jean Lee | Tongshu Zhang | Kunze Wang | Xinghong Guo | Siqu Long | Josiah Poon | Caren Han
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Choosing Transfer Languages for Cross-Lingual Learning
Yu-Hsiang Lin | Chian-Yu Chen | Jean Lee | Zirui Li | Yuyan Zhang | Mengzhou Xia | Shruti Rijhwani | Junxian He | Zhisong Zhang | Xuezhe Ma | Antonios Anastasopoulos | Patrick Littell | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, given a particular task language, it is not clear which language to transfer from, and the standard strategy is to select languages based on ad hoc criteria, usually the intuition of the experimenter. Since a large number of features contribute to the success of cross-lingual transfer (including phylogenetic similarity, typological properties, lexical overlap, or size of available data), even the most enlightened experimenter rarely considers all these factors for the particular task at hand. In this paper, we consider this task of automatically selecting optimal transfer languages as a ranking problem, and build models that consider the aforementioned features to perform this prediction. In experiments on representative NLP tasks, we demonstrate that our model predicts good transfer languages much better than ad hoc baselines considering single features in isolation, and glean insights on what features are most informative for each different NLP tasks, which may inform future ad hoc selection even without use of our method.