Syntax-enhanced Chinese–Vietnamese neural machine translation with linguistic feature template integration

Abstract

Template based translation have become a mainstream technology in the field of neural machine translation. Unlike conventional machine translation methods that employ strategies such as data augmentation or network structure optimization, template-based machine translation excels at incorporating target-side semantics. However, this technological paradigm overly focuses on using the target sentence as a template and fails to effectively utilize the linguistic features in the source sentence and template. To this end, we introduce an innovative method for extracting linguistic features from Chinese–Vietnamese language pair, which serves as a template to steer the translation. This work templates typical language features (modifiers reversed) in Chinese and Vietnamese, and an integration approach is presented for integrating the linguistic feature template into sequence-to-sequence translation framework. The experimental results demonstrate that the proposed method outperforms the strong baseline models with an average 1.15 BLEU score in Chinese–Vietnamese translation tasks, and also achieves significant improvements on other machine translation evaluation metrics. Additionally, the importance of the linguistic feature template has been substantiated through its application in the analysis of Chinese–Vietnamese language characteristics.