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    <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.</abstract>
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%0 Conference Proceedings
%T AMR-TST: Abstract Meaning Representation-based Text Style Transfer
%A Shi, Kaize
%A Sun, Xueyao
%A He, Li
%A Wang, Dingxian
%A Li, Qing
%A Xu, Guandong
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F shi-etal-2023-amr
%X 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.
%R 10.18653/v1/2023.findings-acl.260
%U https://aclanthology.org/2023.findings-acl.260
%U https://doi.org/10.18653/v1/2023.findings-acl.260
%P 4231-4243

Markdown (Informal)

[AMR-TST: Abstract Meaning Representation-based Text Style Transfer](https://aclanthology.org/2023.findings-acl.260) (Shi et al., Findings 2023)

• AMR-TST: Abstract Meaning Representation-based Text Style Transfer (Shi et al., Findings 2023)

ACL

• Kaize Shi, Xueyao Sun, Li He, Dingxian Wang, Qing Li, and Guandong Xu. 2023. AMR-TST: Abstract Meaning Representation-based Text Style Transfer. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4231–4243, Toronto, Canada. Association for Computational Linguistics.