Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models

TL;DR

This paper develops neural machine translation models to convert standard Bangla into regional dialects, aiming to preserve linguistic diversity and enhance communication for dialect speakers.

Contribution

It introduces fine-tuned neural models, especially BanglaT5, for translating Bangla into regional dialects, filling a gap in language technology for these variants.

Findings

BanglaT5 achieved the lowest CER and WER among models tested.

The models effectively captured dialectal nuances in translation.

The study promotes inclusive language technology for regional dialects.

Abstract

The Bangla language includes many regional dialects, adding to its cultural richness. The translation of Bangla Language into regional dialects presents a challenge due to significant variations in vocabulary, pronunciation, and sentence structure across regions like Chittagong, Sylhet, Barishal, Noakhali, and Mymensingh. These dialects, though vital to local identities, lack of representation in technological applications. This study addresses this gap by translating standard Bangla into these dialects using neural machine translation (NMT) models, including BanglaT5, mT5, and mBART50. The work is motivated by the need to preserve linguistic diversity and improve communication among dialect speakers. The models were fine-tuned using the "Vashantor" dataset, containing 32,500 sentences across various dialects, and evaluated through Character Error Rate (CER) and Word Error Rate (WER) metrics. BanglaT5 demonstrated superior performance with a CER of 12.3% and WER of 15.7%, highlighting its effectiveness in capturing dialectal nuances. The outcomes of this research contribute to the development of inclusive language technologies that support regional dialects and promote linguistic diversity.