Unsupervised Machine Translation Using Monolingual Corpora Only

Understand

Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora.

• There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences.
• In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data.
• We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space.