A Neural Attention-Based Encoder-Decoder Approach for English to Bangla Translation

Machine translation (MT) is the process of translating text from one language to another using bilingual data sets and gram-matical rules. Recent works in the field of MT have popular-ized sequence-to-sequence models leveraging neural attention and deep learning. The success of neural attention models is yet to be construed into a robust framework for automated English-to-Bangla translation due to a lack of a comprehensive dataset that encompasses the diverse vocabulary of the Bangla language. In this study, we have proposed an English-to-Bangla MT system using an encoder-decoder attention model using the CCMatrix corpus. Our method shows that this model can outperform tra-ditional SMT and RBMT models with a Bilingual Evaluation Understudy (BLEU) score of 15.68 despite being constrained by the limited vocabulary of the corpus. We hypothesize that this model can be used successfully for state-of-the-art machine trans-lation with a more diverse and accurate dataset. This work can be extended further to incorporate several newer datasets using transfer learning techniques.