On The Evaluation of Machine Translation Systems Trained With Back-Translation

Sergey Edunov, Myle Ott, Marc’Aurelio Ranzato, Michael Auli


Abstract
Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency.
Anthology ID:
2020.acl-main.253
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2836–2846
Language:
URL:
https://aclanthology.org/2020.acl-main.253
DOI:
10.18653/v1/2020.acl-main.253
Bibkey:
Cite (ACL):
Sergey Edunov, Myle Ott, Marc’Aurelio Ranzato, and Michael Auli. 2020. On The Evaluation of Machine Translation Systems Trained With Back-Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2836–2846, Online. Association for Computational Linguistics.
Cite (Informal):
On The Evaluation of Machine Translation Systems Trained With Back-Translation (Edunov et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.253.pdf
Dataset:
 2020.acl-main.253.Dataset.zip
Video:
 http://slideslive.com/38928797
Code
 facebookresearch/evaluation-of-nmt-bt