The need for effective multilingual translation systems has grown due to the rapid development of international communication. Language constraints sometimes restrict accessibility, cooperation, and information sharing between various geographical areas. Conventional translation technologies frequently have trouble comprehending contextual meaning, which leads to translations that are erroneous or awkward. This research suggests a multilingual language conversion system based on generative AI that can efficiently convert text between several languages in order to solve this problem. The suggested system performs intelligent language translation by combining Generative Artificial Intelligence models with Natural Language Processing (NLP) approaches. The system enables users to input text in one language and translate it into another while maintaining sentence structure and semantic meaning. The system's implementation aims to enhance user involvement, contextual comprehension, and translation accuracy. The created program offers an easy-to-use, interactive interface for real-time language translation. When compared to conventional rule-based translation systems, experimental evaluation shows that the suggested method offers better translation quality. The method can be used in a number of fields, including multilingual information systems, international communication, education, and travel. To further increase accessibility worldwide, future improvements might include voice-based translation and the addition of more languages.
Ekambaram et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: