Abstract
While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.- Anthology ID:
- 2022.nlp4pi-1.4
- Volume:
- Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Laura Biester, Dorottya Demszky, Zhijing Jin, Mrinmaya Sachan, Joel Tetreault, Steven Wilson, Lu Xiao, Jieyu Zhao
- Venue:
- NLP4PI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 31–39
- Language:
- URL:
- https://aclanthology.org/2022.nlp4pi-1.4
- DOI:
- 10.18653/v1/2022.nlp4pi-1.4
- Cite (ACL):
- Jessica Lin. 2022. Leveraging World Knowledge in Implicit Hate Speech Detection. In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pages 31–39, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Leveraging World Knowledge in Implicit Hate Speech Detection (Lin, NLP4PI 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.nlp4pi-1.4.pdf