Abstract
This paper describes the system submitted to SemEval 2021 Task 5: Toxic Spans Detection. The task concerns evaluating systems that detect the spans that make a text toxic when detecting such spans are possible. To address the possibly multi-span detection problem, we develop a start-to-end tagging framework on top of RoBERTa based language model. Besides, we design a custom loss function that takes distance into account. In comparison to other participating teams, our system has achieved 69.03% F1 score, which is slightly lower (-1.8 and -1.73) than the top 1(70.83%) and top 2 (70.77%), respectively.- Anthology ID:
- 2021.semeval-1.30
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 258–262
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.30
- DOI:
- 10.18653/v1/2021.semeval-1.30
- Cite (ACL):
- Zhen Wang, Hongjie Fan, and Junfei Liu. 2021. MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 258–262, Online. Association for Computational Linguistics.
- Cite (Informal):
- MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection (Wang et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.semeval-1.30.pdf