@inproceedings{wang-etal-2021-hitmi,
title = "{HITMI}{\&}{T} at {S}em{E}val-2021 Task 5: Integrating Transformer and {CRF} for Toxic Spans Detection",
author = "Wang, Chenyi and
Liu, Tianshu and
Zhao, Tiejun",
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-1.117",
doi = "10.18653/v1/2021.semeval-1.117",
pages = "870--874",
abstract = "This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection. The task aims to accurately locate toxic spans within a text. Using BIO tagging scheme, we model the task as a token-level sequence labeling task. Our system uses a single model built on the model of multi-layer bidirectional transformer encoder. And we introduce conditional random field (CRF) to make the model learn the constraints between tags. We use ERNIE as pre-trained model, which is more suitable for the task accroding to our experiments. In addition, we use adversarial training with the fast gradient method (FGM) to improve the robustness of the system. Our system obtains 69.85{\%} F1 score, ranking 3rd for the official evaluation.",
}
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<abstract>This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection. The task aims to accurately locate toxic spans within a text. Using BIO tagging scheme, we model the task as a token-level sequence labeling task. Our system uses a single model built on the model of multi-layer bidirectional transformer encoder. And we introduce conditional random field (CRF) to make the model learn the constraints between tags. We use ERNIE as pre-trained model, which is more suitable for the task accroding to our experiments. In addition, we use adversarial training with the fast gradient method (FGM) to improve the robustness of the system. Our system obtains 69.85% F1 score, ranking 3rd for the official evaluation.</abstract>
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%0 Conference Proceedings
%T HITMI&T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans Detection
%A Wang, Chenyi
%A Liu, Tianshu
%A Zhao, Tiejun
%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-hitmi
%X This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection. The task aims to accurately locate toxic spans within a text. Using BIO tagging scheme, we model the task as a token-level sequence labeling task. Our system uses a single model built on the model of multi-layer bidirectional transformer encoder. And we introduce conditional random field (CRF) to make the model learn the constraints between tags. We use ERNIE as pre-trained model, which is more suitable for the task accroding to our experiments. In addition, we use adversarial training with the fast gradient method (FGM) to improve the robustness of the system. Our system obtains 69.85% F1 score, ranking 3rd for the official evaluation.
%R 10.18653/v1/2021.semeval-1.117
%U https://aclanthology.org/2021.semeval-1.117
%U https://doi.org/10.18653/v1/2021.semeval-1.117
%P 870-874
Markdown (Informal)
[HITMI&T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans Detection](https://aclanthology.org/2021.semeval-1.117) (Wang et al., SemEval 2021)
ACL