@inproceedings{xie-etal-2021-zjuklab,
title = "{ZJUKLAB} at {S}em{E}val-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning",
author = "Xie, Xin and
Chen, Xiangnan and
Chen, Xiang and
Wang, Yong and
Zhang, Ningyu and
Deng, Shumin and
Chen, Huajun",
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-1.108",
doi = "10.18653/v1/2021.semeval-1.108",
pages = "810--819",
abstract = "This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9{\%} and an accuracy of 92.8{\%}, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches. The code and dataset used in our paper can be found at \url{https://github.com/CheaSim/SemEval2021}. The leaderboard can be found at \url{https://competitions.codalab.org/competitions/26153}.",
}
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<abstract>This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches. The code and dataset used in our paper can be found at https://github.com/CheaSim/SemEval2021. The leaderboard can be found at https://competitions.codalab.org/competitions/26153.</abstract>
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%0 Conference Proceedings
%T ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
%A Xie, Xin
%A Chen, Xiangnan
%A Chen, Xiang
%A Wang, Yong
%A Zhang, Ningyu
%A Deng, Shumin
%A Chen, Huajun
%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 xie-etal-2021-zjuklab
%X This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches. The code and dataset used in our paper can be found at https://github.com/CheaSim/SemEval2021. The leaderboard can be found at https://competitions.codalab.org/competitions/26153.
%R 10.18653/v1/2021.semeval-1.108
%U https://aclanthology.org/2021.semeval-1.108
%U https://doi.org/10.18653/v1/2021.semeval-1.108
%P 810-819
Markdown (Informal)
[ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning](https://aclanthology.org/2021.semeval-1.108) (Xie et al., SemEval 2021)
ACL