AMR-TST: Abstract Meaning Representation-based Text Style Transfer

Shi, K; Sun, X; He, L; Wang, D; Li, Q; Xu, G

AMR-TST: Abstract Meaning Representation-based Text Style Transfer

Shi, K

Sun, X He, L Wang, D Li, Q Xu, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2023, pp. 4231-4243
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Shi, K https://orcid.org/0000-0003-3561-3627
dc.contributor.author	Sun, X
dc.contributor.author	He, L
dc.contributor.author	Wang, D
dc.contributor.author	Li, Q
dc.contributor.author	Xu, G
dc.date.accessioned	2024-03-07T02:55:24Z
dc.date.available	2024-03-07T02:55:24Z
dc.date.issued	2023-01-01
dc.identifier.citation	Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2023, pp. 4231-4243
dc.identifier.isbn	9781959429623
dc.identifier.issn	0736-587X
dc.identifier.uri	http://hdl.handle.net/10453/176240
dc.description.abstract	Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. In this paper, we propose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the ex-plainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.
dc.language	en
dc.relation.ispartof	Proceedings of the Annual Meeting of the Association for Computational Linguistics
dc.rights	info:eu-repo/semantics/openAccess
dc.title	AMR-TST: Abstract Meaning Representation-based Text Style Transfer
dc.type	Conference Proceeding
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-07T02:55:23Z
pubs.publication-status	Published

Abstract:

Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. In this paper, we propose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the ex-plainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.

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http://hdl.handle.net/10453/176240