Off-the-Shelf Unsupervised NMT

6 Nov 2018  ·  Chris Hokamp, Sebastian Ruder, John Glover ·

We frame unsupervised machine translation (MT) in the context of multi-task learning (MTL), combining insights from both directions. We leverage off-the-shelf neural MT architectures to train unsupervised MT models with no parallel data and show that such models can achieve reasonably good performance, competitive with models purpose-built for unsupervised MT... Finally, we propose improvements that allow us to apply our models to English-Turkish, a truly low-resource language pair. read more

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