Neural Machine Translation using Recurrent Neural Network
Debajit Datta1, Preetha Evangeline David2, Dhruv Mittal3, Anukriti Jain4

1Debajit Datta*, B-Tech, Vellore Institute of Technology, Vellore, India.
2Dr. Preetha Evangeline David, Assistant Professor (Sr. Grade), Vellore Institute of Technology, Vellore, India.
3Dhruv Mittal, B-Tech, Vellore Institute of Technology, Vellore, India.
4Anukriti Jain, B-Tech, Vellore Institute of Technology, Vellore, India.

Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1395-1400 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7637049420/2020©BEIESP | DOI: 10.35940/ijeat.D7637.049420
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Abstract: In this era of globalization, it is quite likely to come across people or community who do not share the same language for communication as us. To acknowledge the problems caused by this, we have machine translation systems being developed. Developers of several reputed organizations like Google LLC, have been working to bring algorithms to support machine translations using machine learning algorithms like Artificial Neural Network (ANN) in order to facilitate machine translation. Several Neural Machine Translations have been developed in this regard, but Recurrent Neural Network (RNN), on the other hand, has not grown much in this field. In our work, we have tried to bring RNN in the field of machine translations, in order to acknowledge the benefits of RNN over ANN. The results show how RNN is able to perform machine translations with proper accuracy.
Keywords: Neural Machine Translation, Recurrent Neural Network, Long Short-Term Memory, Speech Recognition.