Abstract
Artificial Intelligence (AI) is cleverly evolving with time and in large volumes of computational and processing strength and excessive call for the final quarter, greater than a billion dollars. Through language translation, people from different parts of the world can communicate, work together, and develop relationships. Machine Translation is undergoing a major transformation thanks to the use of neural networks in machine learning (MT). Looking at the identical aspect, a Machine Translation for English to Hindi has been proposed using the likes of Neural Machine Translation techniques along with attention mechanisms. Neural Machine Translation (NMT) is a modern approach which gives extremely good enhancements in evaluation of traditional system translation techniques. Neural Machine Translation has been capable of rea** massive development over ancient techniques: Rule primarily based model and Statistical Machine Translation. Aiming on the trouble of managing a lengthy distance dependency, attention mechanism is incorporated into the interpretation model, as a result the preprocessing module, encoder-decoder framework, and attention module of the system also are adopted.
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Gururaja, H.S., Seetha, M., Hegde, N., Das, A. (2023). Attention-Based Approach for English to Hindi Translation. In: Seetha, M., Peddoju, S.K., Pendyala, V., Chakravarthy, V.V.S.S.S. (eds) Intelligent Computing and Communication. ICICC 2022. Advances in Intelligent Systems and Computing, vol 1447. Springer, Singapore. https://doi.org/10.1007/978-981-99-1588-0_36
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DOI: https://doi.org/10.1007/978-981-99-1588-0_36
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