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Effects of Six Neural Machine Translators of the Two Business Major Text Types

This study investigates the effects of six major neural machine translators (NMTs) rendering four texts—E-C and C-E directions for the informative and the vocative. By comparing the errors and inadequacies, I found that western-produced NMTs are better at translating E-C and C-E informative texts, though they cannot render culture-loaded words. Baidu, Youdao, and WeChat are similarly good, yet sometimes with confusing blunders. Overall, QQ is the least satisfactory one. For highly vocative E-C texts, all the machine translators are fiascoes since they can only translate the surface meaning, sometimes even confusingly. For C-E vocative texts, DeepL and Google can produce acceptable sentences but retain every piece of information; Chinese-produced NMTs are better at dealing with culture-loaded words. This paper may provide some translation tips for Chinese researchers, the latest trends in NMT for teachers and researchers of the English language, and some insights for technical personnel.