Ключевое слово: «machine translation.»

This article discusses modern software solutions for natural language processing (NLP) with limited resources and a shortage of text data and tools. The relevance of this topic is due to the need to integrate these languages into the digital space to maintain cultural diversity. Machine learning models, including homogeneous transformers and machine translation system, are analyzed using examples of Yakut or Buryat languages. The results show that there are significant differences in the effectiveness of the methods based on the amount of data and the specific situation of the language. The main limitation of the study is the focus on two languages, which narrows the generality of the conclusions. Nevertheless, this work helped to develop methods for further training models of Russian regional languages and emphasized the importance of transferring learning from related languages to overcome the lack of data.

Terminology transfer in technical documentation requires terminological precision, formal consistency, and compliance with domain standards. The paper examines how interface and workflow terms are rendered from English into Russian in the documentation for SDL Trados Studio, a widely used computer-assisted translation environment. The study relies on a comparative analysis of source and target manuals, transformation analysis, and a discourse-oriented reading of instruction sequences. The results show that direct equivalents dominate for menu items and basic workflow labels, while calques are preferred for compound notions related to translation memory and term management. A limited set of items requires adapted borrowings or functional reformulation to preserve usability and avoid ambiguity. The proposed classification supports coherent localization and can be applied in translator training and the development of institutional termbases.