Chinese Sentence Matching with Multiple Alignments and Feature Augmentation

Zuo, Y; Peng, X; Lu, W; Wang, S; Li, Z; Zhang, W; Zhai, Y

Chinese Sentence Matching with Multiple Alignments and Feature Augmentation

Zuo, Y Peng, X

Lu, W Wang, S

Li, Z Zhang, W Zhai, Y

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2022, 2022-July, pp. 1-8
Issue Date:: 2022-01-01

Embargoed

	Filename	Description	Size
	e90120f4-9a05-488d-b07e-05769eda08fe.pdf	Accepted version	625.3 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Embargoed
Open Access

This item is currently unavailable due to the publisher's embargo.

The embargo period expires on 30 Sep 2024

Full metadata record

Field	Value	Language
dc.contributor.author	Zuo, Y
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.contributor.author	Lu, W
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Li, Z
dc.contributor.author	Zhang, W
dc.contributor.author	Zhai, Y
dc.date	2022-07-18
dc.date.accessioned	2023-02-10T04:55:45Z
dc.date.available	2023-02-10T04:55:45Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2022, 2022-July, pp. 1-8
dc.identifier.isbn	9781728186719
dc.identifier.uri	http://hdl.handle.net/10453/166047
dc.description.abstract	Chinese sentence matching is a critical and yet challenging task in natural language processing. Recent work on modeling sentence semantic relations with deep neural models has shown its great potential in improving the performance of sentence matching. However, existing sentence matching methods usually focus on generating word-level sentence representation, which neglects the character-level information and leads to weak semantic representations. Also, they usually capture the interactive features with an attention-based alignment, which are typically implemented on sentence level and neglect the interactions among characters, words and sentences. This paper proposes a novel Chinese sentence matching model with Multiple Alignments and Feature Augmentation (MAFA). Specifically, the model first employs the multi-level embedding layer to accept the character and word sequences of sentences, and introduces the multiple alignment layer to capture the interactions among characters, words and sentences in turn. Then, the feature augmentation layer is applied to combine the interactive features to generate the final semantic matching representations. Finally, the prediction layer is utilized to judge the matching degree of the input sentences. Substantial and extensive experiments are conducted on two real-world data sets to show that MAFA significantly outperforms the competing methods and achieve comnarable nerformance with BERT-based methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	2022 International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN55064.2022.9892521
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Chinese Sentence Matching with Multiple Alignments and Feature Augmentation
dc.type	Conference Proceeding
utslib.citation.volume	2022-July
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-09-30T00:00:00+1000Z
dc.date.updated	2023-02-10T04:55:43Z
pubs.finish-date	2022-07-23
pubs.publication-status	Published
pubs.start-date	2022-07-18
pubs.volume	2022-July

Abstract:

Chinese sentence matching is a critical and yet challenging task in natural language processing. Recent work on modeling sentence semantic relations with deep neural models has shown its great potential in improving the performance of sentence matching. However, existing sentence matching methods usually focus on generating word-level sentence representation, which neglects the character-level information and leads to weak semantic representations. Also, they usually capture the interactive features with an attention-based alignment, which are typically implemented on sentence level and neglect the interactions among characters, words and sentences. This paper proposes a novel Chinese sentence matching model with Multiple Alignments and Feature Augmentation (MAFA). Specifically, the model first employs the multi-level embedding layer to accept the character and word sequences of sentences, and introduces the multiple alignment layer to capture the interactions among characters, words and sentences in turn. Then, the feature augmentation layer is applied to combine the interactive features to generate the final semantic matching representations. Finally, the prediction layer is utilized to judge the matching degree of the input sentences. Substantial and extensive experiments are conducted on two real-world data sets to show that MAFA significantly outperforms the competing methods and achieve comnarable nerformance with BERT-based methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/166047