  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.
