Analyses and Modeling of Neural Machine Translation for English-to-Khasi
Mark S Nonghuloo1, Nagaraja Rao A2
1Mark S. Nonghuloo, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
2Nagaraja Rao A., Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 115-118 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3175079220/2020©BEIESP | DOI: 10.35940/ijrte.B3175.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Language barrier is a common issue faced by humans who move from one community or group to another. Statistical machine translation has enabled us to solve this issue to a certain extent, by formulating models to translate text from one language to another. Statistical machine translation has come a long way but they have their limitations in terms of translating words that belongs to an entirely different context that is not available in the training dataset. This has paved way for neural Machine Translation (NMT), a deep learning approach in solving sequence to sequence translation. Khasi is a language popularly spoken in Meghalaya, a north-east state in India. Its wide and unexplored. In this paper we will discuss about the modeling and analyzing of a NMT base model and a NMT model using Attention mechanism for English to Khasi.
Keywords: Deep Learning, Recurrent Neural Network, LSTM, Neural Machine Translation, Semi-Supervised Machine Learning.