NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension
This work addresses a specific bottleneck in reading comprehension tasks for NLP researchers, but it is incremental as it builds on existing concatenation methods.
The paper tackled the problem of machine reading comprehension by proposing a method that fills options into questions to create fine-grained contexts, which outperformed existing approaches on the SemEval-2021 Task 4 dataset.
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.