THE EVALUTION OF MACHINE TRANSLATION: FROM ALGORITHMIC RULES TO NEURAL NETWORK

M.T.Axmedova

Authors

M.T.Axmedova the teacher of ASIFL

Keywords:

automate translation, human languages, secret codes, intralingual (rewording), interlingual (between languages), and intersemiotic (between sign systems).

Abstract

Machine Translation (MT) has evolved from a Cold War-era cryptographic experiment into an ubiquitous global utility. This article traces the paradigm shifts in MT: Rule-Based Machine Translation (RBMT), Statistical Machine Translation (SMT), and Neural Machine Translation (NMT). By contextualizing these technological milestones within foundational translation literature—such as Eugene Nida’s equivalence theories and Roman Jakobson’s semiotics—this paper examines how computational models have attempted to decode the complexities of human language. Finally, the article addresses the ongoing challenge of translating "the untranslatable"—literary texts, realia, and cultural lacunae.

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