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
Quick Back-Translation for Unsupervised Machine Translation
bbrimacombe/quick-back-translation
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The field of unsupervised machine translation has seen significant advancement from the marriage of the Transformer and the back-translation algorithm.
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages
We train an encoder to gain morphological knowledge of a language and transfer the knowledge to perform unsupervised and weakly-supervised cognate detection tasks with and without the pivot language for the closely-related languages.
Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation
Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation (UNMT).
Unsupervised Mandarin-Cantonese Machine Translation
Advancements in unsupervised machine translation have enabled the development of machine translation systems that can translate between languages for which there is not an abundance of parallel data available.
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model
nxphi47/refine_unsup_multilingual_mt
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Numerous recent work on unsupervised machine translation (UMT) implies that competent unsupervised translations of low-resource and unrelated languages, such as Nepali or Sinhala, are only possible if the model is trained in a massive multilingual environment, where these low-resource languages are mixed with high-resource counterparts.
Leveraging Automated Unit Tests for Unsupervised Code Translation
With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation.
Unsupervised Translation of German--Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language
leukas/wmt21
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Lastly, we experiment with the order in which offline and online back-translation are used to train an unsupervised system, finding that using online back-translation first works better for DE$\rightarrow$DSB by 2. 76 BLEU.
Break-It-Fix-It: Unsupervised Learning for Program Repair
To bridge this gap, we propose a new training approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use the critic to check a fixer's output on real bad inputs and add good (fixed) outputs to the training data, and (ii) we train a breaker to generate realistic bad code from good code.
Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation
This work focuses on comparing different solutions for machine translation on low resource language pairs, namely, with zero-shot transfer learning and unsupervised machine translation.
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task
alexandra-chron/umt-lmu-wmt2020
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WMT (EMNLP) 2020
Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. (2020), using a monolingual pretrained language generation model (on German) and fine-tuning it on both German and Upper Sorbian, before initializing a UNMT model, which is trained with online backtranslation.
