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{"default": {
"description": "Korean Multi-label Hate Speech Dataset - K-MHaS \n a new multi-label dataset for hate speech detection that consists of 109k utterances from Korean online news comments,\n labelled with 8 fine-grained hate speech classes or not hate speech class. \n",
"citation": "@inproceedings{lee-etal-2022-k,\n title = \"K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment\",\n author = \"Lee, Jean and\n Lim, Taejun and\n Lee, Heejun and\n Jo, Bogeun and\n Kim, Yangsok and\n Yoon, Heegeun and\n Han, Soyeon Caren\",\n booktitle = \"Proceedings of the 29th International Conference on Computational Linguistics\",\n month = oct,\n year = \"2022\",\n address = \"Gyeongju, Republic of Korea\",\n publisher = \"International Committee on Computational Linguistics\",\n url = \"https://aclanthology.org/2022.coling-1.311\",\n pages = \"3530--3538\",\n abstract = \"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.\",\n}\n",
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