  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.
