UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language

Ali Hakimi Parizi, Milton King, Paul Cook


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S19-2092.pdf
Data
Hate Speech and Offensive Language