Abstract
This paper describes the KS@LTH system for SemEval-2020 Task 12 OffensEval2: Multilingual Offensive Language Identification in Social Media. We compare mono- and multilingual models based on fine-tuning pre-trained transformer models for offensive language identification in Arabic, Greek, English and Turkish. For Danish, we explore the possibility of fine-tuning a model pre-trained on a similar language, Swedish, and additionally also cross-lingual training together with English.- Anthology ID:
- 2020.semeval-1.270
- 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:
- 2045–2053
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.270
- DOI:
- 10.18653/v1/2020.semeval-1.270
- Cite (ACL):
- Kasper Socha. 2020. KS@LTH at SemEval-2020 Task 12: Fine-tuning Multi- and Monolingual Transformer Models for Offensive Language Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2045–2053, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- KS@LTH at SemEval-2020 Task 12: Fine-tuning Multi- and Monolingual Transformer Models for Offensive Language Detection (Socha, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.semeval-1.270.pdf