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Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection
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Publication Year
2022-01-01
Journal
Proceedings - International Conference on Computational Linguistics, COLING
Publisher
Association for Computational Linguistics (ACL)
Citation
Proceedings - International Conference on Computational Linguistics, COLING, Vol.29 No.1, pp.6644-6655
Mesh Keyword
Detection modelsDetection performanceHuman judgmentsLearn+Reasoning abilitySpeech detectionState-of-the-art performance
All Science Classification Codes (ASJC)
Computational Theory and MathematicsComputer Science ApplicationsTheoretical Computer Science
Abstract
In a hate speech detection model, we should consider two critical aspects in addition to detection performance–bias and explainability. Hate speech cannot be identified based solely on the presence of specific words; the model should be able to reason like humans and be explainable. To improve the performance concerning the two aspects, we propose Masked Rationale Prediction (MRP) as an intermediate task. MRP is a task to predict the masked human rationales–snippets of a sentence that are grounds for human judgment–by referring to surrounding tokens combined with their unmasked rationales. As the model learns its reasoning ability based on rationales by MRP, it performs hate speech detection robustly in terms of bias and explainability. The proposed method generally achieves state-of-the-art performance in various metrics, demonstrating its effectiveness for hate speech detection. Warning: This paper contains samples that may be upsetting.
ISSN
2951-2093
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36861
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85150635665&origin=inward
DOI
https://doi.org/2-s2.0-85150635665
Journal URL
https://aclanthology.org/venues/coling/
Type
Conference
Funding
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF) (NRF-2021S1A5A2A03065899), and also by the NRF of Korea grant funded by the Korean government (MSIT) (NRF-2022R1A2C1007434).
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Sohn, Kyung-Ah Image
Sohn, Kyung-Ah손경아
Department of Software and Computer Engineering
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