BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection
Autor: | Won Ik Cho, Junbum Lee, Jihyung Moon |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Voice activity detection Computer Science - Computation and Language Computer science business.industry 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences language.human_language German Identification (information) Computer Science - Computers and Society Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Artificial intelligence business Computation and Language (cs.CL) computer Natural language processing 0105 earth and related environmental sciences |
Zdroj: | SocialNLP@ACL |
Popis: | Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff's alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks. To be published in SocialNLP@ACL 2020 |
Databáze: | OpenAIRE |
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