TY - GEN
T1 - Neural machine translation for the bangla-english language pair
AU - Hasan, Md Arid
AU - Alam, Firoj
AU - Chowdhury, Shammur Absar
AU - Khan, Naira
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Bangla-to-English
KW - Bidirectional LSTM
KW - Machine Translation
KW - Neural Machine Translation
KW - Transformer
UR - https://www.scopus.com/pages/publications/85082986903
U2 - 10.1109/ICCIT48885.2019.9038381
DO - 10.1109/ICCIT48885.2019.9038381
M3 - Conference contribution
AN - SCOPUS:85082986903
T3 - 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019
BT - 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Computer and Information Technology, ICCIT 2019
Y2 - 18 December 2019 through 20 December 2019
ER -