Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language

Abstract

The Bangla linguistic variety is a fascinating mix of regional dialects that adds to the culturaldiversity of the Bangla-speaking community. Despite extensive study into translating Banglato English, English to Bangla, and Banglish to Bangla in the past, there has been a noticeablegap in translating Bangla regional dialects into standard Bangla. In this study, we set out tofill this gap by creating a collection of 32,500 sentences, encompassing Bangla, Banglish,and English, representing five regional Bangla dialects. Our aim is to translate these regionaldialects into standard Bangla and detect regions accurately. To achieve this, we proposedmodels known as mT5 and BanglaT5 for translating regional dialects into standard Bangla.Additionally, we employed mBERT and Bangla-bert-base to determine the specific regionsfrom where these dialects originated. Our experimental results showed the highest BLEUscore of 69.06 for Mymensingh regional dialects and the lowest BLEU score of 36.75 forChittagong regional dialects. We also observed the lowest average word error rate of 0.1548 for Mymensingh regional dialects and the highest of 0.3385 for Chittagong regional dialects. For region detection, we achieved an accuracy of 85.86% for Bangla-bert-base and 84.36% for mBERT. This is the first large-scale investigation of Bangla regional dialects to Banglamachine translation. We believe our findings will not only pave the way for future work onBangla regional dialects to Bangla machine translation, but will also be useful in solvingsimilar language-related challenges in low-resource language conditions.

Table of Contents

• Experimental Setups
• Dataset Availability
• Results
• Citation
• Contact Information

Experimental Setups

Setup 1: Google Colaboratory

• Environment:
  • Python Version: 3.10.12
  • PyTorch Version: 2.0.1
  • GPU: Tesla T4 (15 GB)
  • RAM: 12.5 GB
  • Disk Space: 64 GB

Setup 2: Jupyter Notebook Environment

• Environment:
  • Python Version: 3.10.12
  • PyTorch Version: 2.0.1
  • GPU: NVIDIA GeForce RTX 3050 (8 GB)
  • RAM: 16 GB
  • Storage: 512 GB NVMe SSD

Dataset Availability

The "Vashantor" dataset, available in both CSV and JSON formats, is now publicly accessible. This dataset provides users with a valuable opportunity for flexible exploration and utilization in various research and analysis endeavors. You can explore and download the dataset at the following link: Vashantor Dataset Feel free to leverage this resource for your research, experiments, or any other analytical purposes. If you have any questions or need further assistance with the dataset, don't hesitate to reach out.

Results

CER, WER, BLEU, METEOR scores of all the Bangla regional dialect translation models

Region	Model	CER	WER	BLEU	METEOR
Chittagong	mT5	0.2308	0.3959	36.75	0.6008
Chittagong	BanglaT5	0.2040	0.3385	44.03	0.6589
Noakhali	mT5	0.2035	0.3870	37.43	0.6073
Noakhali	BanglaT5	0.1863	0.3214	47.38	0.6802
Sylhet	mT5	0.1472	0.2695	51.32	0.7089
Sylhet	BanglaT5	0.1715	0.2802	51.08	0.7073
Barishal	mT5	0.1480	0.2644	48.56	0.7175
Barishal	BanglaT5	0.1497	0.2459	53.50	0.7334
Mymensingh	mT5	0.0796	0.1674	64.74	0.8201
Mymensingh	BanglaT5	0.0823	0.1548	69.06	0.8312

These results represent the evaluation metrics (CER, WER, BLEU, METEOR) for different regions using the mT5 and BanglaT5 models. Explore the performance of each model across various linguistic regions.

Performance Overview of all region detection models

Model	Accuracy	Log Loss	Region	Precision	Recall	F1-Score
mBERT	84.36%	0.9549	Chittagong	0.8779	0.8058	0.8913
			Noakhali	0.9286	0.9437	0.9361
			Sylhet	0.7304	0.9013	0.8072
			Barishal	0.8187	0.5893	0.6847
			Mymensingh	0.9412	0.968	0.9544
Bangla-bert-base	85.86%	0.8804	Chittagong	0.884	0.8486	0.8651
			Noakhali	0.9625	0.9301	0.9461
			Sylhet	0.7388	0.9147	0.8173
			Barishal	0.8373	0.616	0.7091
			Mymensingh	0.9599	0.9653	0.9626

These metrics showcase the performance of mBERT and Bangla-bert-base models in terms of accuracy, log loss, precision, recall, and F1-score across different regions.

Contact Information

For any questions, collaboration opportunities, or further inquiries, please feel free to reach out:

• Fatema Tuj Johora Faria

  • Email: fatema.faria142@gmail.com

• Mukaffi Bin Moin

  • Email: mukaffi28@gmail.com

• Tashreef Muhammad

  • Email: tashreef.muhammad@seu.edu.bd

Citation

If you find the "Vashantor" dataset or the associated research work helpful, please consider citing our paper:

@misc{faria2023vashantor,
  title={Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language},
  author={Fatema Tuj Johora Faria and Mukaffi Bin Moin and Ahmed Al Wase and Mehidi Ahmmed and Md. Rabius Sani and Tashreef Muhammad},
  year={2023},
  eprint={2311.11142},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}