@inproceedings{li-etal-2020-lee-semeval,
title = "Lee at {S}em{E}val-2020 Task 12: A {BERT} Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language",
author = "Li, Junyi and
Zhou, Xiaobing and
Zhang, Zichen",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.273",
doi = "10.18653/v1/2020.semeval-1.273",
pages = "2067--2072",
abstract = "This article describes the system submitted to SemEval 2020 Task 12: OffensEval 2020. This task aims to identify and classify offensive languages in different languages on social media. We only participate in the English part of subtask A, which aims to identify offensive languages in English. To solve this task, we propose a BERT model system based on the transform mechanism, and use the maximum self-ensemble to improve model performance. Our model achieved a macro F1 score of 0.913(ranked 13/82) in subtask A.",
}
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<abstract>This article describes the system submitted to SemEval 2020 Task 12: OffensEval 2020. This task aims to identify and classify offensive languages in different languages on social media. We only participate in the English part of subtask A, which aims to identify offensive languages in English. To solve this task, we propose a BERT model system based on the transform mechanism, and use the maximum self-ensemble to improve model performance. Our model achieved a macro F1 score of 0.913(ranked 13/82) in subtask A.</abstract>
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%0 Conference Proceedings
%T Lee at SemEval-2020 Task 12: A BERT Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language
%A Li, Junyi
%A Zhou, Xiaobing
%A Zhang, Zichen
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F li-etal-2020-lee-semeval
%X This article describes the system submitted to SemEval 2020 Task 12: OffensEval 2020. This task aims to identify and classify offensive languages in different languages on social media. We only participate in the English part of subtask A, which aims to identify offensive languages in English. To solve this task, we propose a BERT model system based on the transform mechanism, and use the maximum self-ensemble to improve model performance. Our model achieved a macro F1 score of 0.913(ranked 13/82) in subtask A.
%R 10.18653/v1/2020.semeval-1.273
%U https://aclanthology.org/2020.semeval-1.273
%U https://doi.org/10.18653/v1/2020.semeval-1.273
%P 2067-2072
Markdown (Informal)
[Lee at SemEval-2020 Task 12: A BERT Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language](https://aclanthology.org/2020.semeval-1.273) (Li et al., SemEval 2020)
ACL