Context-Based Multilingual Translation Technology: on the Example of the Paratranslator Platform

Traditional machine translation systems translate words by processing them at the grammatical and syntactic levels, but they often encounter contextual ambiguities and semantic conflicts. This article covers the theory of context-based translation technology and analyzes the practical application of this approach using the Paratranslator.uz platform as an example. The article describes the scientific foundations of working on translation models based on neural networks, semantic networks, and contextual word units. The Thesaurus, Context, Translator, and NER (Named Entity Recognition) modules of the Paratranslator platform are analyzed and it is shown how translation accuracy is increased through their mutual integration. The research results prove that the context-based translation approach not only ensures linguistic accuracy but also plays an important role in creating translations that are suitable for real communicative situations.

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