Abstract: The scarcity of parallel data is a major obstacle for training high-quality
machine translation systems for low-resource languages. Fortunately, some
low-resource languages are linguistically related or similar to high-resource
languages; these related languages may share many lexical or syntactic
structures. In this work, we exploit this linguistic overlap to facilitate
translating to and from a low-resource language with only monolingual data, in
addition to any parallel data in the related high-resource language. Our
method, NMT-Adapt, combines denoising autoencoding, back-translation and
adversarial objectives to utilize monolingual data for low-resource adaptation.
We experiment on 7 languages from three different language families and show
that our technique significantly improves translation into low-resource
language compared to other translation baselines.