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- W4385571640 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." @default.
- W4385571640 created "2023-08-05" @default.
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- W4385571640 date "2023-01-01" @default.
- W4385571640 modified "2023-09-27" @default.
- W4385571640 title "AMR-TST: Abstract Meaning Representation-based Text Style Transfer" @default.
- W4385571640 doi "https://doi.org/10.18653/v1/2023.findings-acl.260" @default.
- W4385571640 hasPublicationYear "2023" @default.
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