Abstract
In this paper, we describe the team BRUMS entry to OffensEval 2: Multilingual Offensive Language Identification in Social Media in SemEval-2020. The OffensEval organizers provided participants with annotated datasets containing posts from social media in Arabic, Danish, English, Greek and Turkish. We present a multilingual deep learning model to identify offensive language in social media. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility between languages.- Anthology ID:
- 2020.semeval-1.251
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1906–1915
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.251
- DOI:
- 10.18653/v1/2020.semeval-1.251
- Cite (ACL):
- Tharindu Ranasinghe and Hansi Hettiarachchi. 2020. BRUMS at SemEval-2020 Task 12: Transformer Based Multilingual Offensive Language Identification in Social Media. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1906–1915, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- BRUMS at SemEval-2020 Task 12: Transformer Based Multilingual Offensive Language Identification in Social Media (Ranasinghe & Hettiarachchi, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.semeval-1.251.pdf
- Data
- OLID