Combining Advanced Methods in Japanese-Vietnamese Neural Machine Translation
This work addresses the lack of NMT systems for Japanese-Vietnamese, a low-resourced pair, though it appears incremental as it builds on existing methods.
The paper tackled the problem of building neural machine translation systems for the low-resourced Japanese-Vietnamese language pair, achieving significant improvements by combining advanced methods to address data sparsity and rare-word issues.
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.