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. Copyright © 2023 Association for Computational Linguistics.
| Original language | English |
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| Title of host publication | Findings of the Association for Computational Linguistics: ACL 2023 |
| Place of Publication | Canada |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 4231-4243 |
| ISBN (Electronic) | 9781959429623 |
| DOIs | |
| Publication status | Published - 2023 |