Abstract
This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research- Anthology ID:
- 2020.semeval-1.199
- 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:
- 1524–1531
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.199
- DOI:
- 10.18653/v1/2020.semeval-1.199
- Cite (ACL):
- Juan Manuel Pérez, Aymé Arango, and Franco Luque. 2020. ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1524–1531, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection (Pérez et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.semeval-1.199.pdf
- Code
- finiteautomata/offenseval2020
- Data
- OLID