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

• URL: http://arxiv.org/abs/2311.11142v1
• Date: Sat, 18 Nov 2023 18:36:16 GMT
• Title: Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language
• Authors: Fatema Tuj Johora Faria, Mukaffi Bin Moin, Ahmed Al Wase, Mehidi Ahmmed, Md. Rabius Sani, Tashreef Muhammad
• Abstract summary: There has been a noticeable gap in translating Bangla regional dialects into standard Bangla. Our aim is to translate these regional dialects into standard Bangla and detect regions accurately. This is the first large-scale investigation of Bangla regional dialects to Bangla machine translation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Bangla linguistic variety is a fascinating mix of regional dialects that adds to the cultural diversity of the Bangla-speaking community. Despite extensive study into translating Bangla to English, English to Bangla, and Banglish to Bangla in the past, there has been a noticeable gap in translating Bangla regional dialects into standard Bangla. In this study, we set out to fill 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 regional dialects into standard Bangla and detect regions accurately. To achieve this, we proposed models known as mT5 and BanglaT5 for translating regional dialects into standard Bangla. Additionally, we employed mBERT and Bangla-bert-base to determine the specific regions from where these dialects originated. Our experimental results showed the highest BLEU score of 69.06 for Mymensingh regional dialects and the lowest BLEU score of 36.75 for Chittagong 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 Bangla machine translation. We believe our findings will not only pave the way for future work on Bangla regional dialects to Bangla machine translation, but will also be useful in solving similar language-related challenges in low-resource language conditions.

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