Papers › An Effective Approach to Unsupervised Machine Translation
An Effective Approach to Unsupervised Machine Translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre
While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Machine Translation | WMT2014 English-French | SMT + NMT (tuning and joint refinement) | BLEU | 36.2 | #3 of 7 | Archive leaderboard | report |
| Unsupervised Machine Translation | WMT2014 English-German | SMT + NMT (tuning and joint refinement) | BLEU | 22.5 | #1 of 2 | Archive leaderboard | report |
| Unsupervised Machine Translation | WMT2014 French-English | SMT + NMT (tuning and joint refinement) | BLEU | 33.5 | #3 of 7 | Archive leaderboard | report |
| Unsupervised Machine Translation | WMT2014 German-English | SMT + NMT (tuning and joint refinement) | BLEU | 27.0 | #1 of 2 | Archive leaderboard | report |
| Unsupervised Machine Translation | WMT2016 English-German | SMT + NMT (tuning and joint refinement) | BLEU | 26.9 | #3 of 7 | Archive leaderboard | report |
| Unsupervised Machine Translation | WMT2016 German-English | SMT + NMT (tuning and joint refinement) | BLEU | 34.4 | #3 of 7 | 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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