Unsupervised Machine Translation
Unsupervised machine translation is the task of doing machine translation without any translation resources at training time.
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Latest papers with code
Cross-lingual Language Model Pretraining
On unsupervised machine translation, we obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the previous best approach by more than 4 BLEU.
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process.
#2 best model for
Unsupervised Machine Translation on WMT2014 English-German
Unsupervised Statistical Machine Translation
While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018). Despite the potential of this approach for low-resource settings, existing systems are far behind their supervised counterparts, limiting their practical interest.
Phrase-Based & Neural Unsupervised Machine Translation
EMNLP 2018
• facebookresearch/XLM
• 
Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts.
#2 best model for
Machine Translation on WMT2016 English-Russian
Unsupervised Machine Translation Using Monolingual Corpora Only
ICLR 2018
• maochf/MUSE
•
Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data.
#5 best model for
Machine Translation on WMT2016 German-English
Unsupervised Neural Machine Translation
ICLR 2018 • rsennrich/subword-nmt
In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal.
#6 best model for
Machine Translation on WMT2015 English-German
Word Translation Without Parallel Data
ICLR 2018
• jiajunhua/facebookresearch-MUSE
•
State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.