Abstract
This paper presents the different models submitted by the LT@Helsinki team for the SemEval 2020 Shared Task 12. Our team participated in sub-tasks A and C; titled offensive language identification and offense target identification, respectively. In both cases we used the so-called Bidirectional Encoder Representation from Transformer (BERT), a model pre-trained by Google and fine-tuned by us on the OLID and SOLID datasets. The results show that offensive tweet classification is one of several language-based tasks where BERT can achieve state-of-the-art results.- Anthology ID:
- 2020.semeval-1.205
- 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:
- 1569–1575
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.205
- DOI:
- 10.18653/v1/2020.semeval-1.205
- Cite (ACL):
- Marc Pàmies, Emily Öhman, Kaisla Kajava, and Jörg Tiedemann. 2020. LT@Helsinki at SemEval-2020 Task 12: Multilingual or Language-specific BERT?. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1569–1575, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- LT@Helsinki at SemEval-2020 Task 12: Multilingual or Language-specific BERT? (Pàmies et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.semeval-1.205.pdf
- Data
- OLID