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.
Abstract (translated)
孟加拉语包括许多地区方言，这增添了其文化丰富性。将标准的孟加拉语翻译成这些地方方言面临着挑战，因为词汇、发音和句子结构在奇蒂格ong（ Chattogram）、锡莱特（Sylhet）、巴里萨尔（Barishal）、瑙阿卡利（Noakhali）和迈门辛吉（Mymensingh）等地区之间存在显著差异。尽管这些方言对于当地身份认同至关重要，但在技术应用中的代表性却不足。本研究通过使用神经机器翻译(NMT)模型——包括BanglaT5、mT5和mBART50——将标准孟加拉语翻译成这些地方方言来填补这一空白。这项工作旨在保护语言多样性并改善讲不同方言的人之间的交流。 模型在“Vashantor”数据集上进行了微调，该数据集中包含来自各种方言的32,500句句子，并通过字符错误率（CER）和单词错误率（WER）进行评估。BanglaT5表现出色，在CER为12.3%和WER为15.7%的情况下，突出显示了它在捕捉方言细微差别的能力方面具有优势。 这项研究的结果有助于开发包容性语言技术，支持地区方言并促进语言多样性。
URL
https://arxiv.org/abs/2501.05749
