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.

翻译：机器翻译是自然语言处理（NLP）中的核心任务之一，不仅在现实生活中具有广泛应用，也为NLP研究社区的其他任务做出了贡献。近年来，基于Transformer的方法吸引了该领域众多研究者的关注，并在大多数语言对中取得了最先进的成果。本文报告了一种利用短语机制的有效方法——PhraseTransformer，旨在改进强基线模型Transformer，以构建面向越南语-中文平行语料的神经机器翻译（NMT）系统。我们在VLSP 2022竞赛的MT数据集上进行的实验显示，在越南语到中文的翻译中取得了35.3的BLEU分数，在中文到越南语的翻译中取得了33.2的BLEU分数。我们的代码发布于 https://github.com/phuongnm94/PhraseTransformer。