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
Most implemented papers
Incorporating BERT into Neural Machine Translation
bert-nmt/bert-nmt
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ICLR 2020
While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning.
Unsupervised Neural Machine Translation
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.
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
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.
An Effective Approach to Unsupervised Machine Translation
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.
Bilingual Lexicon Induction through Unsupervised Machine Translation
A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods.
Generating Classical Chinese Poems from Vernacular Chinese
whaleloops/interpoetry
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IJCNLP 2019
According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models.
Unsupervised Multilingual Alignment using Wasserstein Barycenter
We study unsupervised multilingual alignment, the problem of finding word-to-word translations between multiple languages without using any parallel data.
Cross-model Back-translated Distillation for Unsupervised Machine Translation
nxphi47/multiagent_crosstranslate
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Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply them differently.
Cross-lingual Retrieval for Iterative Self-Supervised Training
pytorch/fairseq
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NeurIPS 2020
Recent studies have demonstrated the cross-lingual alignment ability of multilingual pretrained language models.
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation
Imagist-Shuo/RRforUNMT
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ACL 2020
The commonly used framework for unsupervised machine translation builds initial translation models of both translation directions, and then performs iterative back-translation to jointly boost their translation performance.
