STAGES OF MACHINE TRANSLATION DEVELOPMENT AND MODERN APPROACHES (SMT, NMT, LLM)

Abstract

This article provides a comprehensive overview of the evolution, methodologies, and current trends in machine translation (MT). It traces the development from early rule-based systems (RBMT) through statistical machine translation (SMT) and neural machine translation (NMT), culminating in the emergence of large language models (LLMs). The study analyzes the theoretical underpinnings, strengths, and limitations of each approach, and compares their translation quality, adaptability, and resource requirements. Special attention is given to the transformative impact of deep learning and transformer architectures on translation accuracy and multilingual support. The article also discusses ongoing challenges, such as low-resource language translation, explainability, and ethical considerations, while suggesting directions for future research, including multimodal and human-in-the-loop translation. Supported by extensive references, this work offers valuable insight into the progression and future prospects of machine translation technologies.