ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation

Autor:	Huang, Kuan-Hao, Iyer, Varun, Hsu, I-Hung, Kumar, Anoop, Chang, Kai-Wei, Galstyan, Aram
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand, automatically annotated paraphrase pairs (e.g., by machine back-translation), usually suffer from the lack of syntactic diversity -- the generated paraphrase sentences are very similar to the source sentences in terms of syntax. In this work, we present ParaAMR, a large-scale syntactically diverse paraphrase dataset created by abstract meaning representation back-translation. Our quantitative analysis, qualitative examples, and human evaluation demonstrate that the paraphrases of ParaAMR are syntactically more diverse compared to existing large-scale paraphrase datasets while preserving good semantic similarity. In addition, we show that ParaAMR can be used to improve on three NLP tasks: learning sentence embeddings, syntactically controlled paraphrase generation, and data augmentation for few-shot learning. Our results thus showcase the potential of ParaAMR for improving various NLP applications. Comment: ACL 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.16585 Zobrazit plný text záznamu View this record from Arxiv