@inproceedings{dai-etal-2020-kungfupanda,
title = "Kungfupanda at {S}em{E}val-2020 Task 12: {BERT}-Based Multi-{T}ask{L}earning for Offensive Language Detection",
author = "Dai, Wenliang and
Yu, Tiezheng and
Liu, Zihan and
Fung, Pascale",
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-1.272",
doi = "10.18653/v1/2020.semeval-1.272",
pages = "2060--2066",
abstract = "Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models. Using a pre-trained language model such as BERT, we can effectively learn the representations for noisy text in social media. Besides, to boost the performance of offensive language detection, we leverage the supervision signals from other related tasks. In the OffensEval-2020 competition, our model achieves 91.51{\%} F1 score in English Sub-task A, which is comparable to the first place (92.23{\%}F1). An empirical analysis is provided to explain the effectiveness of our approaches.",
}
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<abstract>Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models. Using a pre-trained language model such as BERT, we can effectively learn the representations for noisy text in social media. Besides, to boost the performance of offensive language detection, we leverage the supervision signals from other related tasks. In the OffensEval-2020 competition, our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place (92.23%F1). An empirical analysis is provided to explain the effectiveness of our approaches.</abstract>
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%0 Conference Proceedings
%T Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-TaskLearning for Offensive Language Detection
%A Dai, Wenliang
%A Yu, Tiezheng
%A Liu, Zihan
%A Fung, Pascale
%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 dai-etal-2020-kungfupanda
%X Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models. Using a pre-trained language model such as BERT, we can effectively learn the representations for noisy text in social media. Besides, to boost the performance of offensive language detection, we leverage the supervision signals from other related tasks. In the OffensEval-2020 competition, our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place (92.23%F1). An empirical analysis is provided to explain the effectiveness of our approaches.
%R 10.18653/v1/2020.semeval-1.272
%U https://aclanthology.org/2020.semeval-1.272
%U https://doi.org/10.18653/v1/2020.semeval-1.272
%P 2060-2066
Markdown (Informal)
[Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-TaskLearning for Offensive Language Detection](https://aclanthology.org/2020.semeval-1.272) (Dai et al., SemEval 2020)
ACL