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 the top of RoBERTa based language model. Besides, we design a custom loss function which take distance into account. In comparison to other participating teams, our system has achieved 69.03% F1 score, which is slight lower (-1.8 and -1.73) than the top 1 (70.83%) and top 2 (70.77%), respectively.
CITATION STYLE
Wang, Z., Fan, H., & Liu, J. (2021). MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection. In SemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop (pp. 258–262). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.semeval-1.30
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