ALGORITHM OF MULTILINGUAL ELECTRONIC TRANSLATION

Abstract

Multilingual electronic translation is a crucial component of modern applied linguistics and artificial intelligence, enabling effective communication across languages in a globalized digital environment. This paper examines the algorithm of multilingual electronic translation as a multi-stage process that integrates linguistic analysis and machine learning techniques. The proposed algorithm includes input data analysis, tokenization and segmentation, linguistic processing, selection of an appropriate translation model, translation generation, post-processing, and quality evaluation. Special attention is given to neural machine translation models based on the Transformer architecture, which are widely used in contemporary multilingual systems due to their ability to preserve contextual and semantic relationships across languages. The study highlights the importance of post-processing and evaluation metrics in improving translation accuracy and fluency. The presented algorithm demonstrates the effectiveness of modern neural approaches in supporting multiple languages while maintaining translation quality and stylistic consistency.