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

TL;DR

This paper introduces a novel machine reading comprehension model that improves option-question relationship understanding by filling options into questions, leading to better performance on SemEval-2021 Task 4.

Contribution

It proposes a new method of filling options into questions to create a detailed summary, enhancing context understanding over simple concatenation.

Findings

Outperforms existing models significantly

Achieves higher accuracy on SemEval-2021 Task 4 dataset

Demonstrates the effectiveness of fine-grained context modeling

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.