Bangla↔English Machine Translation Using Attention-based Multi-Headed Transformer Model
- 1 Khulna University of Engineering and Technology, Bangladesh
- 2 Gunma University, Japan
- 3 Ulster University, United Kingdom
Abstract
Machine Translation (MT) refers to translate texts or documents from the source language into the target language without human intervention. Any MT model is language-dependent and its development requires grammar, phrase rules, vocabulary, or relevant data for the particular language pair. Hitherto, little research on MT for Bangla-English is reported in the literature, although Bangla is a major language. This study presents a deep learning-based MT system concerning both-way translation for the Bangla-English language pair. The attention-based multi-headed transformer model has been considered in this study due to its significant features of parallelism in input processing. A transformer model consisting of encoders and decoders is adapted by tuning different parameters (especially, number of heads) to identify the best performing model for Bangla to English and vice versa. The proposed model is tested on SUPara benchmark Bangla-English corpus and evaluated the Bilingual Evaluation Understudy (BLEU) score, which is currently the most popular evaluation metric in the MT field. The proposed method is revealed as a promising Bangla-English MT system achieving BLEU scores of 21.42 and 25.44 for Bangla to English and English to Bangla MT cases, respectively.
DOI: https://doi.org/10.3844/jcssp.2021.1000.1010
Copyright: © 2021 Argha Chandra Dhar, Arna Roy, M. A. H. Akhand, Md Abdus Samad Kamal and Nazmul Siddique. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- 3,794 Views
- 1,798 Downloads
- 2 Citations
Download
Keywords
- Deep Learning
- Machine Translation
- Neural Machine Translation
- Transformer Model