Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation

Abstract

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.

Accepted in ACL 2019

References in corpus (7)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
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Cited by in corpus (2)

• Multilingual Denoising Pre-training for Neural Machine Translation
• A Survey on Low-Resource Neural Machine Translation