  Machine Translation is one of the essential tasks in Natural Language
Processing (NLP), which has massive applications in real life as well as
contributing to other tasks in the NLP research community. Recently,
Transformer -based methods have attracted numerous researchers in this domain
and achieved state-of-the-art results in most of the pair languages. In this
paper, we report an effective method using a phrase mechanism,
PhraseTransformer, to improve the strong baseline model Transformer in
constructing a Neural Machine Translation (NMT) system for parallel corpora
Vietnamese-Chinese. Our experiments on the MT dataset of the VLSP 2022
competition achieved the BLEU score of 35.3 on Vietnamese to Chinese and 33.2
BLEU scores on Chinese to Vietnamese data. Our code is available at
https://github.com/phuongnm94/PhraseTransformer.
