Abstract
In this paper we apply a range of approaches to language modeling – including word-level n-gram and neural language models, and character-level neural language models – to the problem of detecting hate speech and offensive language. Our findings indicate that language models are able to capture knowledge of whether text is hateful or offensive. However, our findings also indicate that more conventional approaches to text classification often perform similarly or better.- Anthology ID:
- S19-2092
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 514–518
- Language:
- URL:
- https://aclanthology.org/S19-2092
- DOI:
- 10.18653/v1/S19-2092
- Cite (ACL):
- Ali Hakimi Parizi, Milton King, and Paul Cook. 2019. UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 514–518, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language (Hakimi Parizi et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/S19-2092.pdf
- Data
- Hate Speech and Offensive Language