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


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.
Anthology ID:
2022.coling-1.311
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3530–3538
Language:
URL:
https://aclanthology.org/2022.coling-1.311
DOI:
Bibkey:
Cite (ACL):
Jean Lee, Taejun Lim, Heejun Lee, Bogeun Jo, Yangsok Kim, Heegeun Yoon, and Soyeon Caren Han. 2022. K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3530–3538, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment (Lee et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.311.pdf
Code
 adlnlp/K-MHaS
Data
K-MHaS: Korean Multi-label Hate Speech Dataset