The Application of Computer-Assisted Translation Technology in the Language Service Industry Within the Russian Language Environment

With the rapid development of computer technology, the software industry is experiencing high-speed changes, and international global services have become a fundamental development strategy for international software companies. Translation tools not only improve the speed of translation but also significantly enhance its accuracy. However, developing software products that support globalization has gradually become a bottleneck in software development, as a software product often needs to support several to dozens of localized languages. The translation of different languages, the conversion of encoding methods, and the changes in units of measurement require different support for multi-operating system architectures, which has become a nightmare for many software developers. The traditional localization software development model involves secondary development on the source language version of the software to enhance internationalization support and localized language translation content, but this inevitably leads to extended development cycles and increased costs. By integrating neural network models, similarity algorithms, and the shortest edit distance algorithm, this paper proposes a new model to optimize the translation memory system, thereby significantly improving translation speed. The integrated technology utilizes the powerful learning capabilities of neural network models, combined with the precise matching functions of similarity algorithms and the shortest edit distance algorithm, to more efficiently identify and process repetitive content. Through the collaborative work of neural network models, similarity algorithms, and the shortest edit distance algorithm, the computer-assisted translation (CAT) technology model in the Russian environment not only greatly reduces the repetitive work of translators but also significantly improves overall translation efficiency.