Differentiating DeepSeek AI-generated translations from NMT translations and human translations within Chinese white paper discourse: a computer-based method for analyzing writing style

This study investigates whether distinct “machine-translationese” and “LLM-translationese” exist in Chinese–English government translation by comparing DeepSeek V4Pro, Google neural machine translation, and pre-neural human translation. We built the corpus as 24 minimally overlapping 5,000-word windows per translation condition, yielding 72 matched observations. Using 21 normalized linguistic features selected exclusively within a group-aware training partition, five classifiers were trained and evaluated with a fully held-out, chunk-disjoint test set, and their stability was cross-checked with nested cross-validation and 50 repeated group-aware splits. Naive Bayes and Random Forest emerged as the most consistently above-chance classifiers, while the remaining classifiers were weaker or more sensitive to the specific partition. This indicates that the three translation agents are distinguishable from linguistic form alone. A five-factor multidimensional analysis identified discourse cohesion and causal-deictic linking as the strongest and most stable group contrast separating human translation from both machine systems, alongside four additional, comparatively weaker dimensions capturing nominal/lexical density, nominalization and wh-subordination, lexical diversity and dynamic aspect, and elaborative/predicative complexity. Corrected pairwise comparisons confirmed that human translation exceeds both machine systems on discourse cohesion, and that Google NMT and DeepSeek V4Pro are statistically indistinguishable from one another on most dimensions. A chunk-level bootstrap analysis confirmed that, in five-dimensional factor space, DeepSeek V4Pro and Google NMT sit reliably closer to each other than either does to human translation, supporting a shared machine-translationese distinct from institutional human translation. The study documents differences in stylistic profile and presents the proposed links to reader cognitive load and RLHF-driven hedging as hypotheses emerging from these structural findings. These results provide an empirically grounded, methodologically conservative basis for discussing LLM- and NMT-based translation in official contexts.