Developing a Quranic QA System: Bridging Linguistic Gaps in Urdu Translation Using NLP and Transformer Model

The limited access to Quranic knowledge for Urdu speakers is due to inadequate Natural Language Processing (NLP) tools, which hinder precise Quranic understanding and retrieval. This research introduces a Transformer-based Urdu Quranic Question-Answering (QA) system, a novel approach that enhances semantic accuracy and retrieval precision, unlike existing Arabic-and English-based models. This study primarily leverages Transformer-based technology to develop a context-aware Urdu Quranic chatbot, unlike conventional systems, which primarily support Arabic and English Quranic texts. The system addresses the missing linguistic gaps in Quranic QA by enhancing both precision and semantic interpretation for Urdu users. The system was trained using Fateh Muhammad Jalandhari’s Urdu Quranic translation and fine-tuned with RoBERTafor enhanced semantic text analysis. It integrates TF-IDF with SBERT for improved question-answering performance. The NLP system went through multiple evaluation metrics were used to assess its precision and overall capability. The chatbot achieved high retrieval accuracy with a Mean Average Precision of 0.85, an Exact Match of 0.82, and an F1 Score of 0.88. User satisfaction reached 92%, indicating its effectiveness in providing precise Quranic answers. Future updateswill introduce that include voice detection features, expanded language support, and integration with Tafsir and Hadith databases for improved contextual understanding. This study enhances Urdu Quranic information retrieval by providing an improved NLP-based solution for automated Islamic knowledge dissemination.