ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning

Xin Xie, Xiangnan Chen, Xiang Chen, Yong Wang, Ningyu Zhang, Shumin Deng, Huajun Chen


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.
Anthology ID:
2021.semeval-1.108
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
810–819
Language:
URL:
https://aclanthology.org/2021.semeval-1.108
DOI:
10.18653/v1/2021.semeval-1.108
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.108.pdf
Code
 zjunlp/SemEval2021Task4
Data
ReCAMSQuAD