  We conduct an empirical study of unsupervised neural machine translation
(NMT) for truly low resource languages, exploring the case when both parallel
training data and compute resource are lacking, reflecting the reality of most
of the world's languages and the researchers working on these languages. We
propose a simple and scalable method to improve unsupervised NMT, showing how
adding comparable data mined using a bilingual dictionary along with modest
additional compute resource to train the model can significantly improve its
performance. We also demonstrate how the use of the dictionary to code-switch
monolingual data to create more comparable data can further improve
performance. With this weak supervision, our best method achieves BLEU scores
that improve over supervised results for English$\rightarrow$Gujarati (+18.88),
English$\rightarrow$Kazakh (+5.84), and English$\rightarrow$Somali (+1.16),
showing the promise of weakly-supervised NMT for many low resource languages
with modest compute resource in the world. To the best of our knowledge, our
work is the first to quantitatively showcase the impact of different modest
compute resource in low resource NMT.
