Neural Machine Translation from Bengali Language to English language and vice-versa

Abstract

Bengali ranks among the first ten spoken languages in the world with a native speaker numbering about 230 million people.  With UNESCO declaring 21st February as International Mother Language Day to commemorate the laying down of lives by five Bangladeshi students for the cause of their mother tongue, Bengali has come into the radar of worldwide  attention . Though significant amount of prose, poetry have been written in Bengali language and large number of newspapers in Bengali get published daily, technically it is still considered a Low Resource Language (LRL) unlike English or French which are High Resource Language (HRL). The reason is not far to seek as corpora in varied domains such as short stories, sports, politics, agriculture etc is less in number and even when they are available, the size is less. Machine translation (MT) is difficult to perform in Bengali as parallel corpora from Bengali to other languages and vice versa is few and far between and when they are available they suffer from the problems of size and quality. This work is aimed at implementing one state of the art model in Neural Machine Translation (NMT) which is called the self-attention transformer model to perform translation from English to Bengali and vice versa. Though a couple of research work has been published in the recent years on MT from English to Bengali, they are mostly domain specific. This paper does not focus on any specific domain for NMT from English to Bengali and as such may be conceived as a more of general domain NMT from English to Bengali which is more difficult than domain specific NMT. Performance evaluation of the model was done  using BLEU version-4  vis-à-vis translations of well known English-Bengali MTsystems.