A Deep Learning Approach for Dialect Neutralization in Bengali Academic Writing

Bengali academic writing frequently reflects regional dialectal influences, particularly among students from diverse linguistic regions of Bangladesh. These influences are evident as non-standard vocabulary, spelling forms, and grammatical structures, which make it less clear, formal, and acceptable to automated assessment. This study presents an AI writing assistant based on deep learning that can identify and neutralize regional dialects in Bengali academic writing. The proposed model follows a two-stage architecture that includes dialect classification with transformer-based models and dialect neutralization with sequence-to-sequence text generation models. A custom parallel corpus of dialect-influenced and standardized academic Bengali sentences is constructed through curated sources, synthetic dialect injection, and optional crowdsourced annotation. Experimental evaluation demonstrates promising performance, where the dialect classification stage achieved an accuracy of 0.77 and an F1-Macro score of 0.77 across five regional dialect categories. In the dialect neutralization stage, the system achieved an average Word Error Rate (WER) of 0.3970, a Character Error Rate (CER) of 0.2490, and a BLEU score of 34.52. These results represent a notable reduction in WER relative to single-stage baseline metrics reported in prior dialect-translation literature; because that comparison spans a different translation direction, dataset, and dialect count, it is best read as indicative rather than a controlled ablation, and a matched same-dataset ablation is identified as a priority for follow-up validation. Ultimately, this work illustrates the potential of dialect-conscious normalization for improving the quality of academic texts in Bengali and contributes new data, methods, and empirical evidence to the growing body of Bangla Natural Language Processing research.