HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language
Anna Koufakou, Endang Wahyu Pamungkas, Valerio Basile, Viviana Patti
Abstract
The detection of abusive or offensive remarks in social texts has received significant attention in research. In several related shared tasks, BERT has been shown to be the state-of-the-art. In this paper, we propose to utilize lexical features derived from a hate lexicon towards improving the performance of BERT in such tasks. We explore different ways to utilize the lexical features in the form of lexicon-based encodings at the sentence level or embeddings at the word level. We provide an extensive dataset evaluation that addresses in-domain as well as cross-domain detection of abusive content to render a complete picture. Our results indicate that our proposed models combining BERT with lexical features help improve over a baseline BERT model in many of our in-domain and cross-domain experiments.- Anthology ID:
- 2020.alw-1.5
- Volume:
- Proceedings of the Fourth Workshop on Online Abuse and Harms
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Seyi Akiwowo, Bertie Vidgen, Vinodkumar Prabhakaran, Zeerak Waseem
- Venue:
- ALW
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–43
- Language:
- URL:
- https://aclanthology.org/2020.alw-1.5
- DOI:
- 10.18653/v1/2020.alw-1.5
- Cite (ACL):
- Anna Koufakou, Endang Wahyu Pamungkas, Valerio Basile, and Viviana Patti. 2020. HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 34–43, Online. Association for Computational Linguistics.
- Cite (Informal):
- HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language (Koufakou et al., ALW 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.alw-1.5.pdf
- Data
- HatEval, OLID