Distinguishing Between Formal and Colloquial: A Multilingual BERT Approach to Bengali Language Classification

The Bengali language, rich in history and cultural significance, poses unique challenges in Natural Language Processing (NLP) due to its dual-register structure: Sadhu (formal) and Cholit (colloquial). These registers differ significantly in syntax, vocabulary, and usage, complicating tasks such as text classification, translation, and sentiment analysis. Language models not specifically trained to recognize these distinctions often misinterpret these variations, limiting the accuracy of Bengali NLP tools. To address this, a dataset from Mendeley was used to fine-tune the multilingual BERT (mBERT) model for distinguishing between Sadhu and Cholit registers. The fine-tuned model achieved an accuracy of 94.08%, effectively capturing the subtle lexical and syntactic differences between the two forms. This work advances Bengali NLP, enabling more precise applications in digital communication, automated translation, and linguistic analysis, while contributing to broader advancements in low-resource language processing.