Anastasia Politova

The article explores translation as a means of communication and a tool for shaping the transmitting linguistic culture's narrative in the receiving linguaculture. The subject of the study is communicative, or translation, strategies aimed at transmitting the content expressed with the source language into culturally another linguistic system, retaining cultural and linguistic distinction. The study focuses on Russian and Chinese translations of periodical titles. Additionally, translations of periodical titles between Chinese and five European languages, namely English, French, German, Spanish, and Polish, are chosen as the supplemented material to ensure validity of research outcomes. The analysis revealed that non-mediated translation, i.e., bypassing intermediary languages, is the dominant principle in the translation of Chinese periodical titles used by Russian newspapers. Russian editions of China-governed newspapers have adopted it since the second half of 2022. The comparison of the translation strategies between Chinese and European languages manifested serious divergence: the periodical titles are translated into Chinese through the Cooperation Principle, or domestication, while the translations from Chinese into English, French, German, Spanish, and Polish tend toward the Non-Cooperation Principle, or foreignization; owning to it Chinese periodical titles preserve their linguistic identity when being rendered in other languages. The paper argues that the discovered tendencies stem, firstly, from the language policies of the countries that use the researched languages as their official languages; secondly, from the openness of languages to phonetic borrowings, which affects the semantic transparency of periodical titles in translation; thirdly, from the construction of a stable Chinese media narrative in other languages' environment.

The Russian-Chinese parallel corpus (RuZhCorp) was created in 2016 by sinologists and computational linguists. So far, it has accumulated 1 074 texts and over 4.6 million words that are aligned on a sentence level. To produce word alignment for the entire corpus, we used deep neural networks trained both on the whole RuZhCorp and on a manually aligned at a word level gold dataset. Using the principles presented in previous publications, we compiled the first word-to-word alignment guideline for the Russian-Chinese language pair, which makes the manual alignment process less ambiguous and more consistent. The joint fine-tuning of the LaBSE deep learning model on RuZhCorp and the gold dataset achieved the best AER of 18.9%.