  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.
