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

Nima Pourdamghani, Nada Aldarrab, Marjan Ghazvininejad, Kevin Knight, Jonathan May


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.
Anthology ID:
P19-1293
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3057–3062
Language:
URL:
https://aclanthology.org/P19-1293
DOI:
10.18653/v1/P19-1293
Bibkey:
Cite (ACL):
Nima Pourdamghani, Nada Aldarrab, Marjan Ghazvininejad, Kevin Knight, and Jonathan May. 2019. Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3057–3062, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation (Pourdamghani et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P19-1293.pdf