Abstract Paper
Recently, significant advancements were achieved in Question Answering (QA) systems in several languages. However, QA systems in the Arabic language require further research and improvement because of several challenges and limitations, such as a lack of resources. Especially for QA systems in the Holy Qur’an since it is in classical Arabic and most recent publications are in Modern Standard Arabic. In this research, we report our submission to the Qur’an QA 2022 Shared task-organized with the 5 th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT5). We propose a method for dealing with QA issues in the Holy Qur’an using Deep Learning models. Furthermore, we address the issue of the proposed dataset’s limited sample size by fine-tuning the model several times on several large datasets before fine-tuning it on the proposed dataset achieving 66.9% pRR 54.59% pRR on the development and test sets, respectively.
