Title: Unsupervised Neural Machine Translation,
Abstract: In spite of the recent success of neural machine translation (NMT) in
standard benchmarks, the lack of large parallel corpora poses a major practical
problem for many language pairs. There have been several proposals to alleviate
this issue with, for instance, triangulation and semi-supervised learning
techniques, but they still require a strong cross-lingual signal. In this work,
we completely remove the need of parallel data and propose a novel method to
train an NMT system in a completely unsupervised manner, relying on nothing but
monolingual corpora. Our model builds upon the recent work on unsupervised
embedding mappings, and consists of a slightly modified attentional
encoder-decoder model that can be trained on monolingual corpora alone using a
combination of denoising and backtranslation. Despite the simplicity of the
approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014
French-to-English and German-to-English translation. The model can also profit
from small parallel corpora, and attains 21.81 and 15.24 points when combined
with 100,000 parallel sentences, respectively. Our implementation is released
as an open source project.