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IMI/Publicaţii/CSJM/Ediţii/CSJM v.31, n.1 (91), 2023/

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

Authors: Abdullah Al Shiam, Sadi Md. Redwan, Md. Humaun Kabir, Jungpil Shin
Keywords: Neural Machine Translation (NMT), Machine Translation (MT), Encoder-Decoder Model, Neural Attention.

Abstract

Machine translation (MT) is the process of translating text from one language to another using bilingual data sets and grammatical rules. Recent works in the field of MT have popularized 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 traditional 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 translation with a more diverse and accurate dataset. This work can be extended further to incorporate several newer datasets using transfer learning techniques.

Abdullah Al Shiam
ORCID: https://orcid.org/0000-0002-8787-5584
Department of Computer Science and Engineering,
Sheikh Hasina University,
Netrokona-2400, Bangladesh
E-mail:

Sadi Md. Redwan
ORCID: https://orcid.org/0000-0002-1859-1617
Department of Computer Science and Engineering,
University of Rajshahi,
Rajshahi-6205, Bangladesh.
E-mail:

Md. Humaun Kabir
ORCID: https://orcid.org/0000-0001-7225-0736
Department of Computer Science and Engineering,
Bangamata Sheikh Fojilatunnesa Mujib Science and Technology University,
Jamalpur-2012, Bangladesh.
E-mail:

Jungpil Shin
ORCID: https://orcid.org/0000-0002-7476-2468
School of Computer Science and Engineering,
The University of Aizu Aizuwakamatsu,
Fukushima, 965-8580, Japan.
E-mail:

DOI

https://doi.org/10.56415/csjm.v31.04

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