Quality Assessment of Machine Translation (on the material of Russian-Chinese translation of scientific aerospace texts)
https://doi.org/10.25205/1818-7935-2025-23-3-136-150
Abstract
With the rapid development of artificial intelligence, machine translation has come to be regarded as an effective tool for assisting or even replacing human translation, and its utilization has become increasingly widespread. This study employs a Russian aerospace monograph as the test corpus, introduces the BLEU metric for evaluation, and assesses the translations produced by four prominent machine translation engines: Google Translate, DeepL Translate, Baidu Translate, and ChatGPT Translate. It generalizes and summarizes the prevalent issues encountered at the vocabulary, grammar, and chapter levels when machine software translates Russian scientific texts. Furthermore, the current study proposes improvement suggestions aimed at enhancing the quality of these translations.
About the Authors
Rui Xu
Harbin Institute of Technology
China
China
Xu Rui, Associate Professor
Harbin
Xin Li
Harbin Institute of Technology
China
China
Li Xin, Master
Harbin
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Review
For citations:
Xu R., Li X. Quality Assessment of Machine Translation (on the material of Russian-Chinese translation of scientific aerospace texts). NSU Vestnik. Series: Linguistics and Intercultural Communication. 2025;23(3):136-150. (In Russ.) https://doi.org/10.25205/1818-7935-2025-23-3-136-150
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