# Question-Answering System for Bangla: Fine-tuning BERT-Bangla for a Closed Domain

arXiv: https://arxiv.org/abs/2410.03923 · HTML: https://mlsanity.com/paper/2410.03923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods](https://mlsanity.com/paper/2105.14875/) (2021)

## Built on by (most cited first, 1 of 1)

- [BengaliMCQ: Automatic Generation and Answer Prediction of Academic Multiple-Choice Questions in a Low-Resource Language](https://mlsanity.com/paper/2608.15547/) (2026)

## Abstract (arXiv)

Question-answering systems for Bengali have seen limited development, particularly in domain-specific applications. Leveraging advancements in natural language processing, this paper explores a fine-tuned BERT-Bangla model to address this gap. It presents the development of a question-answering system for Bengali using a fine-tuned BERT-Bangla model in a closed domain. The dataset was sourced from Khulna University of Engineering & Technology's (KUET) website and other relevant texts. The system was trained and evaluated with 2500 question-answer pairs generated from curated data. Key metrics, including the Exact Match (EM) score and F1 score, were used for evaluation, achieving scores of 55.26% and 74.21%, respectively. The results demonstrate promising potential for domain-specific Bengali question-answering systems. Further refinements are needed to improve performance for more complex queries.