Abstract
We propose to improve unsupervised neural machine translation with cross-lingual supervision (), which utilizes supervision signals from high resource language pairs to improve the translation of zero-source languages. Specifically, for training En-Ro system without parallel corpus, we can leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model. % is based on multilingual models which require no changes to the standard unsupervised NMT. Simple and effective, significantly improves the translation quality with a big margin in the benchmark unsupervised translation tasks, and even achieves comparable performance to supervised NMT. In particular, on WMT’14 -tasks achieves 37.6 and 35.18 BLEU score, which is very close to the large scale supervised setting and on WMT’16 -tasks achieves 35.09 BLEU score which is even better than the supervised Transformer baseline.- Anthology ID:
- 2021.naacl-industry.12
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Young-bum Kim, Yunyao Li, Owen Rambow
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–96
- Language:
- URL:
- https://aclanthology.org/2021.naacl-industry.12
- DOI:
- 10.18653/v1/2021.naacl-industry.12
- Cite (ACL):
- Mingxuan Wang, Hongxiao Bai, Hai Zhao, and Lei Li. 2021. Cross-lingual Supervision Improves Unsupervised Neural Machine Translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 89–96, Online. Association for Computational Linguistics.
- Cite (Informal):
- Cross-lingual Supervision Improves Unsupervised Neural Machine Translation (Wang et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.naacl-industry.12.pdf