GENAI FOR NATURAL LANGUAGE TRANSLATION

Abstract

Natural Language Translation (NLT) using Generative Artificial Intelligence is one of the most significant applications of modern artificial intelligence technologies. It enables effective communication between people speaking different languages by automatically translating text while preserving meaning, context, grammar, and tone. This project focuses on the design and development of an intelligent language translation system using advanced deep learning and neural network techniques. Traditional translation systems, such as rule-based and statistical machine translation methods, often face limitations in understanding contextual meaning, idiomatic expressions, and language structure. These methods may generate inaccurate or unnatural translations when handling complex sentences or culturally sensitive content. To overcome these limitations, the proposed system uses Generative AI models based on Transformer architecture, which can learn language patterns from large multilingual datasets and generate more fluent and context-aware translations. The system is trained using bilingual parallel corpora containing sentence pairs from multiple languages. Various preprocessing techniques such as text cleaning, tokenization, normalization, and word embedding are applied to prepare the dataset for training. The model uses supervised learning to improve translation quality and is evaluated using performance metrics including BLEU score, accuracy, precision, and recall. The developed system provides fast, accurate, and natural-sounding translations across different languages. Experimental results demonstrate that the proposed Generative AI-based translation model significantly improves translation quality compared to traditional approaches. This project highlights the importance of AIdriven language translation systems in enhancing global communication, education, healthcare, tourism, and international business interactions.