Iterative Back-Translation for Neural Machine Translation
Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, Trevor Cohn
Abstract
We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 German↔English tasks.- Anthology ID:
- W18-2703
- Volume:
- Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- NGT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 18–24
- Language:
- URL:
- https://aclanthology.org/W18-2703
- DOI:
- 10.18653/v1/W18-2703
- Cite (ACL):
- Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, and Trevor Cohn. 2018. Iterative Back-Translation for Neural Machine Translation. In Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, pages 18–24, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Iterative Back-Translation for Neural Machine Translation (Hoang et al., NGT 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W18-2703.pdf