Word Translation Without Parallel Data (arXiv)


Much work has concentrated on Neural Machine Translation and learning cross-lingual word representations in recent years. Most of these approaches still require large amounts of parallel data. Facebook researchers propose a three-step approach that intelligently combines unsupervised adversarial learning with a way to refine the learned alignment by using frequent words as anchor points and a distance metric that reduces density around hubs in the vector space.


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