Machine‐Translated Language as a Distinctive Translational Variety: Evidence From a Multivariate Analysis of Discourse Connectives Across Text Varieties and Genres

Machine‐translated language is now produced and disseminated on an unprecedented scale, emerging as a new variety of translational language alongside human‐translated language. Yet its linguistic characteristics remain insufficiently understood. Focusing on discourse connectives, this study integrated exploratory correspondence analysis with confirmatory linear mixed‐effects modeling to examine the discourse‐level features of machine‐translated language. The results showed that: (1) machine‐translated language differed significantly from human‐produced language, including both human translations and human‐authored original texts, in academic abstracts and/or government documents, with machine‐translated texts favoring additive connectives and human‐produced texts favoring causal connectives, suggesting a contrast between safer, more general connective choices in machine outputs and more semantically specific discourse marking in human productions; (2) machine‐translated and human‐translated languages, both as varieties of translational language, used more additive connectives in government documents than human‐authored original texts, which showed higher frequencies of adversative and temporal connectives, reflecting shared features of translational language; (3) across most connective categories and genres, no significant differences were found between NMT and LLM‐based MT, suggesting convergence and relative stability in connective preferences despite differences in underlying AI paradigms; and (4) genre significantly moderated connective use across text varieties.