Translator Style in Technical Classics: A Machine-Learning approach on English Translations of Tian Gong Kai Wu

A translator’s style serves as a vital lens for examining the intricacies of the translation process. This is especially notable in translating technical classics, where a tension arises between the source text’s objective nature and the translator’s subjective stylistic choices. To address this tension, objective methods are required to complement subjective interpretation. A machine-learning approach is uniquely suited to this task, as it can impartially quantify stylistic features to reveal strategic decisions that might be obscured by subjective bias. Based on the self-constructed corpora and machine learning approach, this study investigates stylistic patterns in three complete English translations of the Chinese technical classic Tian Gong Kai Wu by applying the CVMI-RRMFT feature selection algorithm to identify the 15 most discriminative linguistic features across lexical, syntactic, and textual levels. These features delineate each translator’s unique style, revealing consistent preferences in linguistic expression. Specifically, Ren’s translation exhibits a scholarly “thick translation” style, marked by lexically rich and modified language, the longest average sentence length with nested syntactic patterns, and prolific use of square brackets for explanatory annotations. Li’s translation reflects a commitment to technical precision and cultural encoding, evident in numerically exact renderings and specialized terminology, a preference for complex phrasal construction, and the unique insertion of traditional Chinese characters within parentheses to assert cultural identity. By contrast, Wang’s translation pursues diplomatic clarity and reader-oriented accessibility through simplified vocabulary, shorter sentences, reduced syntactic complexity, and an overall streamlined use of punctuation to ensure fluent comprehension. Further analysis indicates that these stylistic features reflect not merely unconscious traces, but strategic choices influenced by the dynamic interplay between the translator’s individual agency and external factors. This study demonstrates the efficacy of computational methods in revealing stylistic variation, thereby advancing the empirical study of translation, and enriching the cross-cultural transmission of Chinese technical classics.