Machine Reading Comprehension for the Holy Quran: A Comparative Study

Question Answering (QA) has become a popular topic of research in the Natural Language Processing (NLP) community in recent years. This means that researchers and enthusiasts in the field of NLP have been actively working on developing models and improving existing ones to better answer questions. However, there are fewer studies on Arabic QA compared to other languages, and even fewer on QA for the Quran. BERT is a deep neural network model that has outperformed other models on the SQuAD benchmark. BERT is known for its ability to understand contextual information and provide accurate answers. Therefore, it is a promising model for Quranic QA. In this paper, we will abord to a comparative study of different models based on BERT and used by researchers in the religious field of MRC more precisely the Holy Quran.