Machine Translation from English to Regional Languages Using Transformer Model

Machine Translation (MT) from English to regional languages plays a pivotal role in facilitating global communication and fostering cultural exchange. This paper delves into the objectives, challenges, and advancements within MT, particularly emphasizing Transformer models utilizing sequence-to-sequence architecture. It evaluates the efficiency of such systems in translating English into regional languages like Hindi and Marathi, conducting a comparative analysis with established platforms such as Google Translator and Bing Translator. Through meticulous examination, the paper showcases the reliability and effectiveness of the proposed Machine Translation system. Additionally, it emphasizes the future potential of Machine Translation in bridging cultural gaps and breaking down language barriers. Furthermore, the significance of transfer learning in enhancing Neural Machine Translation (NMT) systems is underscored, highlighting avenues for continued improvement in cross-cultural communication through technological innovation. Overall, this paper contributes to understanding the evolving landscape of Machine Translation and its implications for global interaction and cultural understanding.