  Machine translation systems achieve near human-level performance on some
languages, yet their effectiveness strongly relies on the availability of large
amounts of parallel sentences, which hinders their applicability to the
majority of language pairs. This work investigates how to learn to translate
when having access to only large monolingual corpora in each language. We
propose two model variants, a neural and a phrase-based model. Both versions
leverage a careful initialization of the parameters, the denoising effect of
language models and automatic generation of parallel data by iterative
back-translation. These models are significantly better than methods from the
literature, while being simpler and having fewer hyper-parameters. On the
widely used WMT'14 English-French and WMT'16 German-English benchmarks, our
models respectively obtain 28.1 and 25.2 BLEU points without using a single
parallel sentence, outperforming the state of the art by more than 11 BLEU
points. On low-resource languages like English-Urdu and English-Romanian, our
methods achieve even better results than semi-supervised and supervised
approaches leveraging the paucity of available bitexts. Our code for NMT and
PBSMT is publicly available.
