NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension

Zhixiang Chen, Yikun Lei, Pai Liu, Guibing Guo


Abstract
SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension. Most existing approaches propose to concat question and option together to form a context-aware model. However, we argue that straightforward concatenation can only provide a coarse-grained context for the MRC task, ignoring the specific positions of the option relative to the question. In this paper, we propose a novel MRC model by filling options into the question to produce a fine-grained context (defined as summary) which can better reveal the relationship between option and question. We conduct a series of experiments on the given dataset, and the results show that our approach outperforms other counterparts to a large extent.
Anthology ID:
2021.semeval-1.110
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:
827–832
Language:
URL:
https://aclanthology.org/2021.semeval-1.110
DOI:
10.18653/v1/2021.semeval-1.110
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.110.pdf
Data
DREAMRACEReCAM