  Neural machine translation (NMT) systems have recently obtained state-of-the
art in many machine translation systems between popular language pairs because
of the availability of data. For low-resourced language pairs, there are few
researches in this field due to the lack of bilingual data. In this paper, we
attempt to build the first NMT systems for a low-resourced language
pairs:Japanese-Vietnamese. We have also shown significant improvements when
combining advanced methods to reduce the adverse impacts of data sparsity and
improve the quality of NMT systems. In addition, we proposed a variant of
Byte-Pair Encoding algorithm to perform effective word segmentation for
Vietnamese texts and alleviate the rare-word problem that persists in NMT
systems.
