Neural Graph Matching Networks for Chinese Short Text Matching

Lu Chen, Yanbin Zhao, Boer Lyu, Lesheng Jin, Zhi Chen, Su Zhu, Kai Yu


Abstract
Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance. To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.
Anthology ID:
2020.acl-main.547
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6152–6158
URL:
https://www.aclweb.org/anthology/2020.acl-main.547
DOI:
10.18653/v1/2020.acl-main.547
Bib Export formats:
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PDF:
https://www.aclweb.org/anthology/2020.acl-main.547.pdf
Video:
 http://slideslive.com/38929442