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 baseline experiments on K-MHaS using Korean-BERT-based language models with six different metrics. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.
K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
K-MHaS, a multi-label dataset for Korean hate speech detection, is evaluated using Korean-BERT-based models, with KR-BERT and sub-character tokenization showing superior performance.
- Year
- 2022
- Venue
- COLING 2022 10
- Authors
- 7
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2208.10684v3ARXIV-DEFAULT
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