The Development of Natural Language Processing Technologies and Their Application in Automated Translation Systems

Natural Language Processing (NLP) has become one of the most rapidly developing areas of artificial intelligence and computational linguistics. The continuous growth of digital information, multilingual communication, and global interaction has increased the importance of automated translation systems based on NLP technologies. This article examines the historical development of NLP technologies, the methodological foundations of automated translation systems, and the application of machine learning and deep learning approaches in translation processes. The study analyzes the transition from rule-based systems to statistical and neural machine translation models, highlighting the advantages and limitations of each approach. Particular attention is given to modern neural machine translation systems such as Transformer architectures, multilingual models, and large language models. The article also evaluates the effectiveness of NLP technologies in improving translation quality, semantic understanding, contextual interpretation, and cross-lingual communication. The research is based on scientific literature, factual information, and statistical data from recognized academic sources. The findings indicate that recent advances in NLP technologies significantly improve translation accuracy and efficiency, although challenges related to low-resource languages, contextual ambiguity, and cultural adaptation remain unresolved.