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

Paper

Shi, Kaize, Sun, Xueyao, He, Li, Wang, Dingxian, Li, Qing and Xu, Guandong. 2023. "AMR-TST: Abstract Meaning Representation-based Text Style Transfer." Findings of the Association for Computational Linguistics: ACL 2023. Toronto, Canada 09 - 14 Jul 2023 Canada.
Paper/Presentation Title

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

Presentation TypePaper
AuthorsShi, Kaize, Sun, Xueyao, He, Li, Wang, Dingxian, Li, Qing and Xu, Guandong
Journal Citationpp. 4231-4243
Number of Pages13
Year2023
Place of PublicationCanada
Web Address (URL) of Paperhttps://aclanthology.org/2023.findings-acl.260/
Web Address (URL) of Conference Proceedingshttps://aclanthology.org/volumes/2023.findings-acl/
Conference/EventFindings of the Association for Computational Linguistics: ACL 2023
Event Details
Findings of the Association for Computational Linguistics: ACL 2023
Delivery
In person
Event Date
09 to end of 14 Jul 2023
Event Location
Toronto, Canada
Event Web Address (URL)
Abstract

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 explainability 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.

Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Byline AffiliationsUniversity of Technology Sydney
Hong Kong Polytechnic University, China
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