A Hybrid Optimization Approach for Neural Machine Translation Using LSTM+RNN with MFO for Under Resource Language (Telugu)

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Published: Sep 1, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i7.8002
Keywords:
Neural Machine Translation, statistical machine translation, Recurrent Neural Network, Moth Flame Optimization, Long short-term memory

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Srisudha Garugu
Research Scholar, Department of Computer Science & System Engineering, Andhra University College of Engineering (A), Andhra University, Visakhapatnam-530003, India
D. Lalitha Bhaskari
Professor Department of Computer Science & System Engineering, Coordinator, IQAC, Andhra University College of Engineering (A), Andhra University, Visakhapatnam-530003, India

Abstract

NMT (Neural Machine Translation) is an innovative approach in the field of machine translation, in contrast to SMT (statistical machine translation) and Rule-based techniques which has resulted annotable improvements. This is because NMT is able to overcome many of the shortcomings that are inherent in the traditional approaches. The Development of NMT has grown tremendously in the recent years but NMT performance remain under optimal when applied to low resource language pairs like Telugu, Tamil and Hindi. In this work a proposedmethod fortranslating pairs (Telugu to English) is attempted, an optimal approach which enhancesthe accuracy and execution time period.A hybrid method approach utilizing Long short-term memory (LSTM) and traditional Recurrent Neural Network (RNN) are used for testing and training of the dataset. In the event of long-range dependencies, LSTM will generate more accurate results than a standard RNN would endure and the hybrid technique enhances the performance of LSTM. LSTM is used during the encoding and RNN is used in decoding phases of NMT. Moth Flame Optimization (MFO) is utilized in the proposed system for the purpose of providing the encoder and decoder model with the best ideal points for training the data.

Article Details

How to Cite
Garugu, S. ., & Bhaskari, D. L. . (2023). A Hybrid Optimization Approach for Neural Machine Translation Using LSTM+RNN with MFO for Under Resource Language (Telugu). International Journal on Recent and Innovation Trends in Computing and Communication, 11(7), 354–366. https://doi.org/10.17762/ijritcc.v11i7.8002
Issue
Vol. 11 No. 7 (2023)
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Articles

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