Machine learning and deep learning techniques in Arabic question answering systems: innovations and challenges

Research on Arabic question answering improves information access, promotes language variety, advances Arabic Language Processing NLP technology, and has educational, cultural, economic, and societal implications. This study delves deeply into Artificial Intelligence (AI)-based Arabic Question Answering Systems (AQAS), with an emphasis on the use of machine learning (ML) and deep learning (DL) technologies to improve Arabic language processing and comprehension. A careful analysis of twelve qualifying studies done between 2018 and 2023 identifies considerable advances in the use of advanced computational approaches to address Arabic’s distinctive linguistic problems. This work is particularly relevant for practitioners and researchers in the fields of AI and NLP, as well as professionals interested in the implications of AI technologies for Arabic language processing. Moreover, we recognize that the impact of AQAS extends beyond academia; it has significant implications for various sectors, including education, technology, and information access. Through this comprehensive examination, we aim to lay the groundwork for ongoing innovation and development in AQAS. Our study emphasizes the necessity for high-quality, diverse datasets that encompass the linguistic variety and dialectal variations of Arabic. We also explore the potential of hybrid AI models for improved semantic analysis, while acknowledging the computational challenges faced by state-of-the-art AI models. The study also acknowledges the computational challenges faced by state-of-the-art AI models and suggests future research directions focused on developing lightweight, efficient models, enhancing semantic analysis, and ensuring the fairness and equity of AI applications. Despite substantial progress, the study identifies gaps in handling linguistic nuances, the scarcity of annotated datasets, and the limited exploration of innovative AI techniques. It calls for a collaborative effort to enrich the Arabic Question Answering (AQA) datasets, improve computational efficiency and advance the semantic understanding capabilities of AQAS.