Neural Machine Translation for the Bangla-English Language Pair
Due to the rapid advancement of different neural network architectures, the task of automated translation from one language to another is now in a new era of Machine Translation (MT) research. In the last few years, Neural Machine Translation (NMT) architectures have proven to be successful for resource-rich languages, trained on a large dataset of translated sentences, with variations of NMT algorithms used to train the model. In this study, we explore different NMT algorithms-Bidirectional Long Short Term Memory (LSTM) and Transformer based NMT, to translate the Bangla to English language pair. For the experiments, we used different datasets and our experimental results outperform the existing performance by a large margin on different datasets. We also investigated the factors affecting the data quality and how they influence the performance of the models. It shows a promising research avenue to enhance NMT for the Bangla-English language pair.