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A Benchmark Arabic Dataset for Arabic Question Classification using AAFAQ Framework.

Mariam Essam Abdelaziz¹, Mohanad A Deif^2,3, Shabbab Ali Algamdi⁴

¹Department of Computer Science, College of Information Technology, Misr University for Science and Technology (MUST), P.O. Box 77, Giza, Egypt.

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|August 18, 2025

Summary

This summary is machine-generated.

This study introduces the AAFAQ dataset for Arabic Natural Language Processing (NLP), enhancing question classification and generation. The dataset significantly improves model performance in understanding complex Arabic questions.

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Area of Science:

Computational Linguistics
Artificial Intelligence
Natural Language Processing

Background:

Arabic Natural Language Processing (NLP) faces challenges due to complex morphology and scarce annotated resources.
Existing datasets lack comprehensive linguistic and cognitive annotations for advanced Arabic question analysis.

Purpose of the Study:

To introduce the AAFAQ dataset, a novel resource for Arabic question classification and semantic understanding.
To support research in advanced Arabic question analysis, including intent and cognitive level classification.

Main Methods:

Development of an open-domain dataset with 5,009 Modern Standard Arabic (MSA) questions.
Annotation based on the AAFAQ Framework, covering 11 linguistic and cognitive aspects.
Validation through fine-tuning state-of-the-art models like AraBERT and integration into generative QA systems.

Main Results:

AraBERT achieved 100% accuracy in Question Particle Type and 94.95% in Intent classification.
Integration with Alpaca+Gemma-9B Unsloth improved BLEU (+37.6%), ROUGE-1 (+132%), and BERTScore (+17.3%) in generative QA.
The dataset demonstrates effectiveness in both classification and generation tasks for Arabic NLP.

Conclusions:

The AAFAQ dataset is a valuable benchmark for advancing Arabic question comprehension and related NLP tasks.
The dataset has potential applications in education, cognitive computing, and multilingual AI.
Future extensions will address underrepresented categories to further enhance the dataset's scope.