  We conduct an empirical study of neural machine translation (NMT) for truly
low-resource languages, and propose a training curriculum fit for cases 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. Previously, unsupervised NMT, which employs back-translation (BT)
and auto-encoding (AE) tasks has been shown barren for low-resource languages.
We demonstrate that leveraging comparable data and code-switching as weak
supervision, combined with BT and AE objectives, result in remarkable
improvements for low-resource languages even when using only modest compute
resources. The training curriculum proposed in this work achieves BLEU scores
that improve over supervised NMT trained on the same backbone architecture by
+12.2 BLEU for English to Gujarati and +3.7 BLEU for English to Kazakh,
showcasing the potential of weakly-supervised NMT for the low-resource
languages. When trained on supervised data, our training curriculum achieves a
new state-of-the-art result on the Somali dataset (BLEU of 29.3 for Somali to
English). We also observe that adding more time and GPUs to training can
further improve performance, which underscores the importance of reporting
compute resource usage in MT research.
