Papers › Unsupervised Machine Translation Using Monolingual Corpora Only
Unsupervised Machine Translation Using Monolingual Corpora Only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.
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Code
Syntology Ran 3 of 14 code samples harvested from 3 repositories linked to this paper; 11 have no recorded run. Of those that ran: 1 ran · violated contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
14 samples harvested; 3 ran; 0 honoured the contract we drafted; 11 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | WMT2016 English-German | Unsupervised S2S with attention | BLEU score | 9.64 | #11 of 12 | Archive leaderboard | report |
| Machine Translation | WMT2016 German-English | Unsupervised S2S with attention | BLEU score | 13.33 | #7 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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