Year: 2023
Type: conference-paper
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-theart 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4385571640