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

arXiv: https://arxiv.org/abs/1906.05683 · HTML: https://mlsanity.com/paper/1906.05683/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation](https://mlsanity.com/paper/2304.11501/) (2023)
- [Majority Voting with Bidirectional Pre-translation For Bitext Retrieval](https://mlsanity.com/paper/2103.06369/) (2021)
- [Letz Translate: Low-Resource Machine Translation for Luxembourgish](https://mlsanity.com/paper/2303.01347/) (2023)
- [Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages](https://mlsanity.com/paper/2103.13272/) (2021)
- [Sometimes We Want Translationese](https://mlsanity.com/paper/2104.07623/) (2021)
- [Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search](https://mlsanity.com/paper/2309.16141/) (2023)
- [The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation](https://mlsanity.com/paper/2403.18031/) (2024)
- [Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages](https://mlsanity.com/paper/2206.04439/) (2022)
- [Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay](https://mlsanity.com/paper/2606.11786/) (2026)

## Abstract (arXiv)

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.