Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation

Cabezudo M.A.S.; Inácio M.L.; Pardo T.A.S. · INLG 2024 - 17th International Natural Language Generation Conference, Proceedings of the Conference (2024)

Resumen

Meaning Representation (AMR) is a meaning representation (MR) designed to abstract away from syntax, allowing syntactically different sentences to share the same AMR graph. Unlike other MRs, existing AMR corpora typically link one AMR graph to a single reference. This paper investigates the value of paraphrase generation in low-resource AMR-to-Text generation by testing various paraphrase generation strategies and evaluating their impact. The findings show that paraphrase generation significantly outperforms the baseline and traditional data augmentation methods, even with fewer training instances. Human evaluations indicate that this strategy often produces syntactic-based paraphrases and can exceed the performance of previous approaches. Additionally, the paper releases a paraphrase-extended version of the AMR corpus.

Instituciones: Pontificia Universidad Católica del Perú