@inproceedings{chen-etal-2020-neural-graph,
title = "Neural Graph Matching Networks for {C}hinese Short Text Matching",
author = "Chen, Lu and
Zhao, Yanbin and
Lyu, Boer and
Jin, Lesheng and
Chen, Zhi and
Zhu, Su and
Yu, Kai",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.547/",
doi = "10.18653/v1/2020.acl-main.547",
pages = "6152--6158",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Neural Graph Matching Networks for Chinese Short Text Matching
%A Chen, Lu
%A Zhao, Yanbin
%A Lyu, Boer
%A Jin, Lesheng
%A Chen, Zhi
%A Zhu, Su
%A Yu, Kai
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-neural-graph
%X 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.
%R 10.18653/v1/2020.acl-main.547
%U https://aclanthology.org/2020.acl-main.547/
%U https://doi.org/10.18653/v1/2020.acl-main.547
%P 6152-6158
Markdown (Informal)
[Neural Graph Matching Networks for Chinese Short Text Matching](https://aclanthology.org/2020.acl-main.547/) (Chen et al., ACL 2020)
ACL