  Unsupervised neural machine translation (NMT) has attracted a lot of
attention recently. While state-of-the-art methods for unsupervised translation
usually perform well between similar languages (e.g., English-German
translation), they perform poorly between distant languages, because
unsupervised alignment does not work well for distant languages. In this work,
we introduce unsupervised pivot translation for distant languages, which
translates a language to a distant language through multiple hops, and the
unsupervised translation on each hop is relatively easier than the original
direct translation. We propose a learning to route (LTR) method to choose the
translation path between the source and target languages. LTR is trained on
language pairs whose best translation path is available and is applied on the
unseen language pairs for path selection. Experiments on 20 languages and 294
distant language pairs demonstrate the advantages of the unsupervised pivot
translation for distant languages, as well as the effectiveness of the proposed
LTR for path selection. Specifically, in the best case, LTR achieves an
improvement of 5.58 BLEU points over the conventional direct unsupervised
method.
