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
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages
For most language combinations, parallel data is either scarce or simply unavailable.
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.
Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study
In this paper, we show that initializing the embedding layer of UNMT models with cross-lingual embeddings shows significant improvements in BLEU score over existing approaches with embeddings randomly initialized.
Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining
Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages.
Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora
Cross-lingual text representations have gained popularity lately and act as the backbone of many tasks such as unsupervised machine translation and cross-lingual information retrieval, to name a few.
Unsupervised Machine Translation On Dravidian Languages
We show that unifying the writing systems is essential in unsupervised translation between the Dravidian languages.
From Unsupervised Machine Translation To Adversarial Text Generation
B-GAN is able to generate a distributed latent space representation which can be paired with an attention based decoder to generate fluent sentences.
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.
SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task.
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages
We outperform all current state-of-the-art unsupervised baselines for these languages, achieving gains of up to 14. 4 BLEU.
