Implementation of Qur’anic Question Answering System Based on the BERT Model

The Holy Qur'an is the oldest comprehensive Arabic book of recommendation for Muslims worldwide. Provide knowledge and information used in various ways. People often utilize the Holy Qur'an, considered a reliable and trustful legislated text, for education and to meet their needs and inquiries of the Muslim community. Like extracting an answer span from the provided passage, The Qur'an has the potential to captivate the curiosity of non-Muslims and propel them towards exploring a vast array of topics and pursuing answers. Over the past few years, Question Answering (Q.A.) has drawn much attention from the NLP community. Researchers and experts have developed various Qur'anic Question Answering (QAA) systems. Nevertheless, the main challenge in the Arabic language is the need for more resources, making it difficult to provide highly accurate Arabic QA systems. The first Qur’an Question Answering shared task workshop, “Qur’an QA 2022,” aims to promote state-of-the-art research on Qur'anic question answering QA in general and machine reading comprehension MRC in particular. It aims to develop models to extract questions answering the holy Qur’an passages. This research paper motivated by this task, suggests an ensemble learning model based on Arabic-supported versions of BERT, which will be implemented using the KNIME platform. We aim to use this model for Arabic Question Answering.; as a result, we get 0.488 for the AUC and 0.946 for accuracy.