  Neural machine translation is the current state-of-the-art in machine
translation. Although it is successful in a resource-rich setting, its
applicability for low-resource language pairs is still debatable. In this
paper, we explore the effect of different techniques to improve machine
translation quality when a parallel corpus is as small as 324 000 sentences,
taking as an example previously unexplored Russian-Tatar language pair. We
apply such techniques as transfer learning and semi-supervised learning to the
base Transformer model, and empirically show that the resulting models improve
Russian to Tatar and Tatar to Russian translation quality by +2.57 and +3.66
BLEU, respectively.
