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
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
18–24
URL:
https://www.aclweb.org/anthology/W18-2703
DOI:
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