  Given a rough, word-by-word gloss of a source language sentence, target
language natives can uncover the latent, fully-fluent rendering of the
translation. In this work we explore this intuition by breaking translation
into a two step process: generating a rough gloss by means of a dictionary and
then `translating' the resulting pseudo-translation, or `Translationese' into a
fully fluent translation. We build our Translationese decoder once from a
mish-mash of parallel data that has the target language in common and then can
build dictionaries on demand using unsupervised techniques, resulting in
rapidly generated unsupervised neural MT systems for many source languages. We
apply this process to 14 test languages, obtaining better or comparable
translation results on high-resource languages than previously published
unsupervised MT studies, and obtaining good quality results for low-resource
languages that have never been used in an unsupervised MT scenario.
