@inproceedings{bai-zhou-2020-byteam,
title = "{BY}team at {S}em{E}val-2020 Task 5: Detecting Counterfactual Statements with {BERT} and Ensembles",
author = "Bai, Yang and
Zhou, Xiaobing",
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-1.82",
doi = "10.18653/v1/2020.semeval-1.82",
pages = "640--644",
abstract = "We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals). This paper examines different approaches and models towards detecting counterfactual statements classification. We choose the Bert model. However, the output of Bert is not a good summary of semantic information, so in order to obtain more abundant semantic information features, we modify the upper layer structure of Bert. Finally, our system achieves an accuracy of 88.90{\%} and F1 score of 86.30{\%} by hard voting, which ranks 6th on the final leader board of the in subtask 1 competition.",
}
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<abstract>We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals). This paper examines different approaches and models towards detecting counterfactual statements classification. We choose the Bert model. However, the output of Bert is not a good summary of semantic information, so in order to obtain more abundant semantic information features, we modify the upper layer structure of Bert. Finally, our system achieves an accuracy of 88.90% and F1 score of 86.30% by hard voting, which ranks 6th on the final leader board of the in subtask 1 competition.</abstract>
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%0 Conference Proceedings
%T BYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles
%A Bai, Yang
%A Zhou, Xiaobing
%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 bai-zhou-2020-byteam
%X We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals). This paper examines different approaches and models towards detecting counterfactual statements classification. We choose the Bert model. However, the output of Bert is not a good summary of semantic information, so in order to obtain more abundant semantic information features, we modify the upper layer structure of Bert. Finally, our system achieves an accuracy of 88.90% and F1 score of 86.30% by hard voting, which ranks 6th on the final leader board of the in subtask 1 competition.
%R 10.18653/v1/2020.semeval-1.82
%U https://aclanthology.org/2020.semeval-1.82
%U https://doi.org/10.18653/v1/2020.semeval-1.82
%P 640-644
Markdown (Informal)
[BYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles](https://aclanthology.org/2020.semeval-1.82) (Bai & Zhou, SemEval 2020)
ACL