Abstract:
Language translation is in high demand for a variety of reasons, including business travel
and news comprehension, in this modern-day when people are increasingly reliant on
technology. Bengali is the world's seventh most widely spoken language; yet, when
compared to a language with more resources, such as English, the work done on Bengali
machine translation falls short. The accuracy could be increased utilizing a state-of-the-art
technique. We were inspired to investigate English to Bengali machine translation after
finding research gaps. We utilized Neural Machine Translation namely BiLSTM. The
encoder and decoder both employed BiLSTM; the encoder and decoder map the English
sentences to Bengali sentences. In the decoder, the attention mechanism was implemented
for better mapping. We found that this method works well for accurate machine translation.
Because the sequences of the input language of the BiLSTM, are taken from both the left
and the right, the mapping becomes more accurate. We compared our approaches to other
standard results of English-Bengali translation. And finally, we showed that our result
outperformed other claimed results.
