Using DeepSeek as a Russian-Chinese Translation Tool

Abstract

The subject of the research is the semantic, grammatical, and pragmatic characteristics of translations generated by DeepSeek in comparison with DeepL and Google Translate. The object is machine translation in the Russian–Chinese language pair using generative language models. The relevance is determined by the contradiction between the expanding use of large language models in translation and the insufficiently defined boundaries of their effectiveness with typologically and culturally distant languages. The aim is to determine the boundaries of DeepSeek's effective application in both translation directions. The objectives include characterizing the model's technological features, comparing cognitive mechanisms of language processing by humans and artificial intelligence, and empirically testing translation quality against DeepL and Google Translate according to semantic accuracy, grammatical correctness, and pragmatic adequacy. The study employed comparative analysis, cognitive modeling, and interpretive analysis on a corpus of 30 phraseological and culturally marked units. The scientific novelty lies in the systematization of knowledge about generative neural networks in translation theory and in the comparative assessment of three systems on a unified corpus according to three complementary criteria. The author's contribution consists in refining the understanding of similarities and differences between human and machine cognitive mechanisms. The main findings are as follows: DeepSeek outperforms DeepL and Google Translate in conveying idioms and cultural realia, however its functional adaptation may lead to semantic shifts, necessitating professional post-editing in terminologically dense texts. The most justified application is producing draft translations and finding contextual equivalents, while final verification should remain with the human translator.