Abstract
State-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings. This occurs, typically, due to classifiers overemphasizing source-specific information that negatively impacts its domain invariance. Prior work has attempted to penalize terms related to hate-speech from manually curated lists using feature attribution methods, which quantify the importance assigned to input terms by the classifier when making a prediction. We, instead, propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a domain classifier, which learns to differentiate between domains, and feature-attribution scores for hate-speech classes, yielding consistent improvements in cross-domain evaluation.- Anthology ID:
- 2022.coling-1.578
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6656–6666
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.578
- DOI:
- Cite (ACL):
- Tulika Bose, Nikolaos Aletras, Irina Illina, and Dominique Fohr. 2022. Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6656–6666, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection (Bose et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/landing_page/2022.coling-1.578.pdf
- Data
- HatEval