Self-Supervised Neural Machine Translation

Dana Ruiter, Cristina España-Bonet, Josef van Genabith


Abstract
We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations. This is done in a self-supervised way without parallel data, in such a way that both tasks enhance each other during training. The method is language independent, introduces no additional hyper-parameters, and achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training.
Anthology ID:
P19-1178
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1828–1834
Language:
URL:
https://www.aclweb.org/anthology/P19-1178
DOI:
10.18653/v1/P19-1178
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1178.pdf
Video:
 https://vimeo.com/384515284