Unsupervised Machine Translation
30 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
Most implemented papers
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Word Translation Without Parallel Data
facebookresearch/MUSE
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ICLR 2018
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.
Cross-lingual Language Model Pretraining
huggingface/transformers
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NeurIPS 2019
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.
Unsupervised Machine Translation Using Monolingual Corpora Only
facebookresearch/MUSE
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ICLR 2018
By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data.
Phrase-Based & Neural Unsupervised Machine Translation
facebookresearch/UnsupervisedMT
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EMNLP 2018
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.
Unsupervised Translation of Programming Languages
We train our model on source code from open source GitHub projects, and show that it can translate functions between C++, Java, and Python with high accuracy.
MASS: Masked Sequence to Sequence Pre-training for Language Generation
microsoft/MASS
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Pre-training and fine-tuning, e. g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks.
Multilingual Denoising Pre-training for Neural Machine Translation
pytorch/fairseq
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This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.
A Probabilistic Formulation of Unsupervised Text Style Transfer
cindyxinyiwang/deep-latent-sequence-model
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ICLR 2020
Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes.
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).
