  Recent work achieved remarkable results in training neural machine
translation (NMT) systems in a fully unsupervised way, with new and dedicated
architectures that rely on monolingual corpora only. In this work, we propose
to define unsupervised NMT (UNMT) as NMT trained with the supervision of
synthetic bilingual data. Our approach straightforwardly enables the use of
state-of-the-art architectures proposed for supervised NMT by replacing
human-made bilingual data with synthetic bilingual data for training. We
propose to initialize the training of UNMT with synthetic bilingual data
generated by unsupervised statistical machine translation (USMT). The UNMT
system is then incrementally improved using back-translation. Our preliminary
experiments show that our approach achieves a new state-of-the-art for
unsupervised machine translation on the WMT16 German--English news translation
task, for both translation directions.

Computation and Language