Edit Distance Based Curriculum Learning for Paraphrase Generation

Sora Kadotani, Tomoyuki Kajiwara, Yuki Arase, Makoto Onizuka


Abstract
Curriculum learning has improved the quality of neural machine translation, where only source-side features are considered in the metrics to determine the difficulty of translation. In this study, we apply curriculum learning to paraphrase generation for the first time. Different from machine translation, paraphrase generation allows a certain level of discrepancy in semantics between source and target, which results in diverse transformations from lexical substitution to reordering of clauses. Hence, the difficulty of transformations requires considering both source and target contexts. Experiments on formality transfer using GYAFC showed that our curriculum learning with edit distance improves the quality of paraphrase generation. Additionally, the proposed method improves the quality of difficult samples, which was not possible for previous methods.
Anthology ID:
2021.acl-srw.24
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
229–234
Language:
URL:
https://aclanthology.org/2021.acl-srw.24
DOI:
10.18653/v1/2021.acl-srw.24
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-srw.24.pdf