@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://aclanthology.org/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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection
%A Wang, Zhen
%A Fan, Hongjie
%A Liu, Junfei
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-medai
%X 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.
%R 10.18653/v1/2021.semeval-1.30
%U https://aclanthology.org/2021.semeval-1.30/
%U https://doi.org/10.18653/v1/2021.semeval-1.30
%P 258-262
Markdown (Informal)
[MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection](https://aclanthology.org/2021.semeval-1.30/) (Wang et al., SemEval 2021)
ACL