Abstract
Investigators publish new report on machine translation. According to news reporting from Kunming, People’s Republic of China, by NewsRx journalists, research stated, “As the typical distant language pair, Chinese and Vietnamese vary widely in syntactic structure, which significantly influences the performance of Chinese-Vietnamese machine translation.” The news editors obtained a quote from the research from Yunnan Minzu University: “To address this problem, we present a simple approach with a pre-reordering model for closing syntactic gaps of the Chinese-Vietnamese language pair. Specifically, we first propose an algorithm for recognizing the modifier inverse, one of the most representative syntactic different in Chinese-Vietnamese language pair. Then we pre-train a pre-reordering model based on the former recognition algorithm and incorporate it into the attention-based translation framework for syntactic different reordering.” According to the news editors, the research concluded: “We conduct empirical studies on ChineseVietnamese neural machine translation task, the results show that our approach achieves average improvement of 2.75 BLEU points in translation quality over the baseline model. In addition, the translation fluency can be significantly improved by over 2.44 RIBES points.”
NETL
NSTL
Ios Press