  This paper proposes a novel multilingual multistage fine-tuning approach for
low-resource neural machine translation (NMT), taking a challenging
Japanese--Russian pair for benchmarking. Although there are many solutions for
low-resource scenarios, such as multilingual NMT and back-translation, we have
empirically confirmed their limited success when restricted to in-domain data.
We therefore propose to exploit out-of-domain data through transfer learning,
by using it to first train a multilingual NMT model followed by multistage
fine-tuning on in-domain parallel and back-translated pseudo-parallel data. Our
approach, which combines domain adaptation, multilingualism, and
back-translation, helps improve the translation quality by more than 3.7 BLEU
points, over a strong baseline, for this extremely low-resource scenario.
