An Arabic question generation system based on a shared BERT-base encoder-decoder architecture

A Question Generation System (QGS) is a sophisticated piece of AI technology designed to automatically generate questions from a given text, document, or context.  Recently, this technology has gained significant attention in various fields, including education, and content creation.  As AI continues to evolve, these systems are likely to become even more advanced and viewed as an inherent part of any modern e-learning or knowledge assessment system.  In this research paper, we showcase the effectiveness of leveraging pre-trained checkpoints for Arabic questions generation.  We propose a Transformer-based sequence-to-sequence model that seamlessly integrates with publicly accessible pre-trained AraBERT checkpoints.  Our study focuses on evaluating the advantages of initializing our model, encompassing both the encoder and decoder, with these checkpoints.  As resources for Arabic language are still limited and the publicly datasets for question generation systems in Arabic are not available, we collected our dataset for this task from various existing question answering, we used this latter to train and test our model.  The experimental results show that our model yields performance was able to outperform existing Arabic question generation models in terms of the BLEU and METEOR scores,by achieving 20.29 as BLEU score and 30.73 for METEOR.  Finally, we assessed the capability of our model to generate contextually relevant questions.