Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas
Abstract Deep learning has significantly advanced the question-answering (QA) systems across various sectors. However, Arabic-language systems for Hajj-related fatwas (non-binding Islamic legal opinions issued by muftis) remain underdeveloped. This paper introduces Hajj-FQA, a benchmark Arabic datas...
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| Format: | Article |
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Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00128-w |
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| author | Hayfa A. Aleid Aqil M. Azmi |
| author_facet | Hayfa A. Aleid Aqil M. Azmi |
| author_sort | Hayfa A. Aleid |
| collection | DOAJ |
| description | Abstract Deep learning has significantly advanced the question-answering (QA) systems across various sectors. However, Arabic-language systems for Hajj-related fatwas (non-binding Islamic legal opinions issued by muftis) remain underdeveloped. This paper introduces Hajj-FQA, a benchmark Arabic dataset specifically designed to develop HajjBot - a specialized chatbot for fatwas QA during the annual Hajj pilgrimage. The dataset captures the unique linguistic and jurisprudential characteristics of pilgrims’ inquiries, enabling accurate, domain-specific responses. We present a comprehensive quantitative analysis of the dataset’s construction methodology and its distinctive question-answer patterns. Evaluation using multilingual and Arabic-specific language models across three tasks - machine reading comprehension (MRC), duplicate question detection (DQD), and duplicate answer detection (DAD) - with 10-fold cross-validation demonstrates the practical utility of Hajj-FQA. Results show exceptional performance in classification tasks (AraBERTv0.2 achieved a precision score of 99.19% for DQD and 99.26% for DAD) and strong extractive answering capability with an $$F\text {-score}$$ F -score of 72.78%. While generative performance reached $$\text {BERT-}F$$ BERT- F score of 71.4% (AraBART), MRC variability highlights challenges in religious reasoning. These findings establish Hajj-FQA as both: (1) a critical resource for developing specialized fatwa chatbots like HajjBot, and (2) a benchmark for Arabic religious QA systems. The dataset directly addresses the urgent need for accurate, automated fatwa assistance during Hajj, while providing insights for future improvements in Islamic NLP applications. |
| format | Article |
| id | doaj-art-68bb5f3cb1fa41c7813e6059dd425860 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-68bb5f3cb1fa41c7813e6059dd4258602025-08-20T03:46:20ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-07-0137612810.1007/s44443-025-00128-wHajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwasHayfa A. Aleid0Aqil M. Azmi1Department of Computer Science, College of Computer & Information Sciences, King Saud UniversityDepartment of Computer Science, College of Computer & Information Sciences, King Saud UniversityAbstract Deep learning has significantly advanced the question-answering (QA) systems across various sectors. However, Arabic-language systems for Hajj-related fatwas (non-binding Islamic legal opinions issued by muftis) remain underdeveloped. This paper introduces Hajj-FQA, a benchmark Arabic dataset specifically designed to develop HajjBot - a specialized chatbot for fatwas QA during the annual Hajj pilgrimage. The dataset captures the unique linguistic and jurisprudential characteristics of pilgrims’ inquiries, enabling accurate, domain-specific responses. We present a comprehensive quantitative analysis of the dataset’s construction methodology and its distinctive question-answer patterns. Evaluation using multilingual and Arabic-specific language models across three tasks - machine reading comprehension (MRC), duplicate question detection (DQD), and duplicate answer detection (DAD) - with 10-fold cross-validation demonstrates the practical utility of Hajj-FQA. Results show exceptional performance in classification tasks (AraBERTv0.2 achieved a precision score of 99.19% for DQD and 99.26% for DAD) and strong extractive answering capability with an $$F\text {-score}$$ F -score of 72.78%. While generative performance reached $$\text {BERT-}F$$ BERT- F score of 71.4% (AraBART), MRC variability highlights challenges in religious reasoning. These findings establish Hajj-FQA as both: (1) a critical resource for developing specialized fatwa chatbots like HajjBot, and (2) a benchmark for Arabic religious QA systems. The dataset directly addresses the urgent need for accurate, automated fatwa assistance during Hajj, while providing insights for future improvements in Islamic NLP applications.https://doi.org/10.1007/s44443-025-00128-wChatbotsArabic question-answeringArabic NLPDatasetFatwaHajj |
| spellingShingle | Hayfa A. Aleid Aqil M. Azmi Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas Journal of King Saud University: Computer and Information Sciences Chatbots Arabic question-answering Arabic NLP Dataset Fatwa Hajj |
| title | Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas |
| title_full | Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas |
| title_fullStr | Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas |
| title_full_unstemmed | Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas |
| title_short | Hajj-FQA: A benchmark Arabic dataset for developing question-answering systems on Hajj fatwas |
| title_sort | hajj fqa a benchmark arabic dataset for developing question answering systems on hajj fatwas |
| topic | Chatbots Arabic question-answering Arabic NLP Dataset Fatwa Hajj |
| url | https://doi.org/10.1007/s44443-025-00128-w |
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