@inproceedings{wang-etal-2021-medai,
    title = "{M}ed{AI} at {S}em{E}val-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection",
    author = "Wang, Zhen  and
      Fan, Hongjie  and
      Liu, Junfei",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.semeval-1.30/",
    doi = "10.18653/v1/2021.semeval-1.30",
    pages = "258--262",
    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."
}