A Review of Question-Answering Systems Using Deep Learning in the Arabic Language

Ali Aloqla; Reda Khalifa; Wajdi Alghamdi

doi:10.48084/etasr.14229

Authors

Ali Aloqla Computer Information Systems Department, King Abdulaziz University, Jeddah, Saudi Arabia
Reda Khalifa Computer Information Technology Department, King Abdulaziz University, Jeddah, Saudi Arabia
Wajdi Alghamdi Computer Information Technology Department, King Abdulaziz University, Jeddah, Saudi Arabia

Volume: 15 | Issue: 6 | Pages: 29214-29228 | December 2025 | https://doi.org/10.48084/etasr.14229

Received: 21 August 2025 | Revised: 17 September 2025 and 20 September 2025 | Accepted: 24 September 2025 | Online: 8 December 2025

Corresponding author: Ali Aloqla

Abstract

Question-Answering (QA) has become a pivotal topic in Natural Language Processing (NLP), facilitating machines' comprehension and response to human inquiries in natural language. Although QA systems for English and other high-resource languages have been extensively studied, Arabic QA remains under-investigated and faces several linguistic and technical challenges. This paper offers an extensive analysis of deep learning-based Arabic QA systems, emphasizing extractive, generative, and hybrid architectures. This study analyzes the fundamental issues in Arabic processing, outlines essential datasets, and provides a classification of QA methodologies. Furthermore, it identifies several research gaps, including the absence of domain-specific models, limited generative question answering, and insufficient use of retrieval-augmented architectures. To overcome these deficiencies, a Fatwa-based dataset, currently under development, can serve as a resource for future research on domain-specific Arabic QA. This study also delineates prospective trajectories, emphasizing the promise of Retrieval-Augmented Generation (RAG), few-shot learning, and dialect-aware models in propelling the discipline forward.

Keywords:

Arabic NLP, QA, deep learning, RAG, natural language understanding, transformer

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