Abstract—The applications of recurrent neural networks in machine translation are increasing in natural language processing. Besides other languages, Bangla language contains a large amount of vocabulary. Improvement of English to Bangla machine translation would be a significant contribution to Bangla Language processing. This paper describes an architecture of English to Bangla machine translation system. The system has been implemented with the encoder-decoder recurrent neural network. The model uses a knowledge-based context vector for the mapping of English and Bangla words. Performances of the model based on activation functions are measured here. The best performance is achieved for the linear activation function in encoder layer and the tanh activation function in decoder layer. From the execution of GRU and LSTM layer, GRU performed better than LSTM. The attention layers are enacted with softmax and sigmoid activation function. The approach of the model outperforms the previous state-of-the-art systems in terms of cross-entropy loss metrics. The reader can easily find out the structure of the machine translation of English to Bangla and the efficient activation functions from the paper.
Index Terms—English, Bangla, machine translation, RNN, natural language processing.
Shaykh Siddique, Tahmid Ahmed, Md. Rifayet Azam Talukder, and Md. Mohsin Uddin are with the Department of Computer Science and Engineering, East West University, Dhaka-1212, Bangladesh (e-mail: {shaykhsiddiqee, tahmidahmedtdk, rifayetewucse}@gmail.com, mmuddin@ewubd.edu).
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Cite: Shaykh Siddique, Tahmid Ahmed, Md. Rifayet Azam Talukder, and Md. Mohsin Uddin, "English to Bangla Machine Translation Using Recurrent Neural Network," International Journal of Future Computer and Communication vol. 9, no. 2, pp. 46-51, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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