@inproceedings{zhu-etal-2021-hitsz,
title = "{HITSZ}-{HLT} at {S}em{E}val-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection",
author = "Zhu, Qinglin and
Lin, Zijie and
Zhang, Yice and
Sun, Jingyi and
Li, Xiang and
Lin, Qihui and
Dang, Yixue and
Xu, Ruifeng",
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-1.63/",
doi = "10.18653/v1/2021.semeval-1.63",
pages = "521--526",
abstract = "This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text`s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83{\%}, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice."
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<abstract>This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text‘s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83%, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.</abstract>
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%0 Conference Proceedings
%T HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection
%A Zhu, Qinglin
%A Lin, Zijie
%A Zhang, Yice
%A Sun, Jingyi
%A Li, Xiang
%A Lin, Qihui
%A Dang, Yixue
%A Xu, Ruifeng
%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 zhu-etal-2021-hitsz
%X This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text‘s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83%, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.
%R 10.18653/v1/2021.semeval-1.63
%U https://aclanthology.org/2021.semeval-1.63/
%U https://doi.org/10.18653/v1/2021.semeval-1.63
%P 521-526
Markdown (Informal)
[HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection](https://aclanthology.org/2021.semeval-1.63/) (Zhu et al., SemEval 2021)
ACL