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
- Editors:
- Jad Kabbara, Haitao Lin, Amandalynne Paullada, Jannis Vamvas
- 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
- Cite (ACL):
- Sora Kadotani, Tomoyuki Kajiwara, Yuki Arase, and Makoto Onizuka. 2021. Edit Distance Based Curriculum Learning for Paraphrase Generation. In 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, pages 229–234, Online. Association for Computational Linguistics.
- Cite (Informal):
- Edit Distance Based Curriculum Learning for Paraphrase Generation (Kadotani et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.acl-srw.24.pdf
- Data
- GYAFC