Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated
  Translation of Bangla Regional Dialects to Bangla Language
Fatema Tuj Johora Faria Mukaffi Bin Moin Ahmed Al Wase Mehidi Ahmmed Md. Rabius Sani Tashreef Muhammad
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.