K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
Abstract
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.
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.
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