  To improve low-resource Neural Machine Translation (NMT) with multilingual
corpora, training on the most related high-resource language only is often more
effective than using all data available (Neubig and Hu, 2018). However, it is
possible that an intelligent data selection strategy can further improve
low-resource NMT with data from other auxiliary languages. In this paper, we
seek to construct a sampling distribution over all multilingual data, so that
it minimizes the training loss of the low-resource language. Based on this
formulation, we propose an efficient algorithm, Target Conditioned Sampling
(TCS), which first samples a target sentence, and then conditionally samples
its source sentence. Experiments show that TCS brings significant gains of up
to 2 BLEU on three of four languages we test, with minimal training overhead.
