  There are several approaches for improving neural machine translation for
low-resource languages: Monolingual data can be exploited via pretraining or
data augmentation; Parallel corpora on related language pairs can be used via
parameter sharing or transfer learning in multilingual models; Subword
segmentation and regularization techniques can be applied to ensure high
coverage of the vocabulary. We review these approaches in the context of an
asymmetric-resource one-to-many translation task, in which the pair of target
languages are related, with one being a very low-resource and the other a
higher-resource language. We test various methods on three artificially
restricted translation tasks -- English to Estonian (low-resource) and Finnish
(high-resource), English to Slovak and Czech, English to Danish and Swedish --
and one real-world task, Norwegian to North S\'ami and Finnish. The experiments
show positive effects especially for scheduled multi-task learning, denoising
autoencoder, and subword sampling.
