Abstract
Neural Machine Translation (NMT) is the latest Machine Translation procedure that promotes exceptional upgrades compared to Rule-Based and Statistical Machine Translation procedures by conquering many shortcomings in conventional methods. NMT accepts preferences in a straightforward design and can catch lengthy relations in a sentence, demonstrating an immense possibility of turning into a different standard. Particularly for Indian Languages, NMT techniques show greater accuracy. The Attention Mechanism drastically improves the accuracy. The way to utilize the Attention Mechanism that guarantees the speed and accuracy of the translation together has likewise become a significant issue for scientists to explain. In this paper, we implement Attention Mechanism in the Machine Translation for Indian Languages and improve accuracy. Our primary objective is to test the model with the Hindi-English pair. Our NMT for this language pair showed a BLEU score of 0.8274. For future enhancement, we plan to work on other Indian Language pairs.
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Velpuri, S.Y., Karanwal, S., Anita, R. (2022). Neural Machine Translation Using Attention Mechanism. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P., Goel, L. (eds) Proceedings of International Conference on Recent Trends in Computing . Lecture Notes in Networks and Systems, vol 341. Springer, Singapore. https://doi.org/10.1007/978-981-16-7118-0_61
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DOI: https://doi.org/10.1007/978-981-16-7118-0_61
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