MACHINE TRANSLATION SYSTEMS: ANALYSIS OF RULE-BASED, STATISTICAL, AND NEURAL APPROACHE

Abstract

This article provides a comprehensive analysis of the evolution of machine translation (MT) methodologies, focusing on the transition from rule-based machine translation (RBMT) to statistical machine translation (SMT) and the current dominance of neural machine translation (NMT). In the context of globalization and the increasing need to bridge language barriers, MT systems have become essential tools for automated translation across different languages. The paper provides a detailed comparison of these three key approaches, examining their methodologies, advantages, and limitations.