Paper Info
Title
S3-NET: SRU-Based Sentence and Self-Matching Networks for Machine Reading Comprehension
Abstract
Machine reading comprehension question answering (MRC-QA) is the task of understanding the context of a given passage to find a correct answer within it. A passage is composed of several sentences; therefore, the length of the input sentence becomes longer, leading to diminished performance. In this article, we propose S3-NET, which adds sentence-based encoding to solve this problem. S3-NET, which is based on a simple recurrent unit architecture, is a deep learning model that solves the MRC-QA by applying matching network to sentence-level encoding. In addition, S3-NET utilizes self-matching networks to compute attention weight for its own recurrent neural network sequences. We perform MRC-QA for the SQuAD dataset of English and MindsMRC dataset of Korean. The experimental results show that for SQuAD, the S3-NET model proposed in this article produces 71.91% and 74.12% exact match and 81.02% and 82.34% F1 in single and ensemble models, respectively, and for MindsMRC, our model achieves 69.43% and 71.28% exact match and 81.53% and 82.77% F1 in single and ensemble models, respectively.
Year
DOI
Venue
2020
10.1145/3365679
ACM Transactions on Asian and Low-Resource Language Information Processing
Keywords
DocType
Volume
Korean MRC-QA,Machine reading comprehension,hierarchical model,question answering,sentence and self-matching network,simple recurrent unit
Journal
19
Issue
ISSN
Citations 
3
2375-4699
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Cheon-Eum Park113.05
Heejun Song200.34
Changki Lee327926.18