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.
@article{arxiv.1805.07133,
title = {Combining Advanced Methods in Japanese-Vietnamese Neural Machine Translation},
author = {Thi-Vinh Ngo and Thanh-Le Ha and Phuong-Thai Nguyen and Le-Minh Nguyen},
journal= {arXiv preprint arXiv:1805.07133},
year = {2020}
}