Analysis Of Neural Machine Translation For English To Hindi Using Long Short-Term Memory Model And Transformer Model

Abstract

Neural Machine Translation (NMT) has revolutionized the field of machine translation by delivering significantly improved accuracy and fluency compared to traditional approaches. This research paper focuses specifically on the task of English-to-Hindi translation using advanced NMT techniques. We examine the development and evaluation of specialized NMT systems designed for this linguistically challenging language pair, taking into account the unique grammatical structures and cultural nuances of both English and Hindi. By leveraging largescale parallel corpora and cutting-edge neural network architectures, our work introduces innovative methods to enhance both translation quality and computational efficiency. Our experimental results, evaluated through standard metrics including BLEU score, demonstrate the proposed NMT models' effectiveness inaccurately capturing semantic meaning while maintaining natural fluency in the translated output. Furthermore, this study explores the broader implications of these findings for crosslinguistic communication and information accessibility, particularly given Hindi's increasing prominence in our globalized digital landscape. Ultimately, this research advances the state of English-Hindi neural machine translation and underscores NMT's  transformative potential in enabling effective multilingual communication and knowledge sharing worldwide.