Unsupervised Machine Translation
32 papers with code • 9 benchmarks • 4 datasets
Unsupervised machine translation is the task of doing machine translation without any translation resources at training time.
( Image credit: Phrase-Based & Neural Unsupervised Machine Translation )
Benchmarks
These leaderboards are used to track progress in Unsupervised Machine Translation
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Libraries
Use these libraries to find Unsupervised Machine Translation models and implementations
WMT 2014
WMT 2016
CCMatrix
WMT 2016 News
Latest papers with no code
The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation
Unsupervised on-the-fly back-translation, in conjunction with multilingual pretraining, is the dominant method for unsupervised neural machine translation.
Boosting Unsupervised Machine Translation with Pseudo-Parallel Data
Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge.
On the Role of Parallel Data in Cross-lingual Transfer Learning
While prior work has established that the use of parallel data is conducive for cross-lingual learning, it is unclear if the improvements come from the data itself, or if it is the modeling of parallel interactions that matters.
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning
Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc.
Flow-Adapter Architecture for Unsupervised Machine Translation
The primary novelties of our model are: (a) capturing language-specific sentence representations separately for each language using normalizing flows and (b) using a simple transformation of these latent representations for translating from one language to another.
On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation
Modern unsupervised machine translation (MT) systems reach reasonable translation quality under clean and controlled data conditions.
Multilingual Unsupervised Neural Machine Translation with Denoising Adapters
We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs.
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.
Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation
Modern unsupervised machine translation systems mostly train their models by generating synthetic parallel training data from large unlabeled monolingual corpora of different languages through various means, such as iterative back-translation.
Paraphrase Generation as Unsupervised Machine Translation
Then based on the paraphrase pairs produced by these UMT models, a unified surrogate model can be trained to serve as the final \sts model to generate paraphrases, which can be directly used for test in the unsupervised setup, or be finetuned on labeled datasets in the supervised setup.
