@inproceedings{wu-wu-2020-warren,
title = "Warren at {S}em{E}val-2020 Task 4: {ALBERT} and Multi-Task Learning for Commonsense Validation",
author = "Wu, Yuhang and
Wu, Hao",
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-1.79",
doi = "10.18653/v1/2020.semeval-1.79",
pages = "620--625",
abstract = "This paper describes our system in subtask A of SemEval 2020 Shared Task 4. We propose a reinforcement learning model based on MTL(Multi-Task Learning) to enhance the prediction ability of commonsense validation. The experimental results demonstrate that our system outperforms the single-task text classification model. We combine MTL and ALBERT pretrain model to achieve an accuracy of 0.904 and our model is ranked 16th on the final leader board of the competition among the 45 teams.",
}
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<abstract>This paper describes our system in subtask A of SemEval 2020 Shared Task 4. We propose a reinforcement learning model based on MTL(Multi-Task Learning) to enhance the prediction ability of commonsense validation. The experimental results demonstrate that our system outperforms the single-task text classification model. We combine MTL and ALBERT pretrain model to achieve an accuracy of 0.904 and our model is ranked 16th on the final leader board of the competition among the 45 teams.</abstract>
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%0 Conference Proceedings
%T Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation
%A Wu, Yuhang
%A Wu, Hao
%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 wu-wu-2020-warren
%X This paper describes our system in subtask A of SemEval 2020 Shared Task 4. We propose a reinforcement learning model based on MTL(Multi-Task Learning) to enhance the prediction ability of commonsense validation. The experimental results demonstrate that our system outperforms the single-task text classification model. We combine MTL and ALBERT pretrain model to achieve an accuracy of 0.904 and our model is ranked 16th on the final leader board of the competition among the 45 teams.
%R 10.18653/v1/2020.semeval-1.79
%U https://aclanthology.org/2020.semeval-1.79
%U https://doi.org/10.18653/v1/2020.semeval-1.79
%P 620-625
Markdown (Informal)
[Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation](https://aclanthology.org/2020.semeval-1.79) (Wu & Wu, SemEval 2020)
ACL