@inproceedings{mostafa-mohamed-2022-gof,
    title = "{GOF} at Qur{'}an {QA} 2022: Towards an Efficient Question Answering For The Holy Qu{'}ran In The {A}rabic Language Using Deep Learning-Based Approach",
    author = "Mostafa, Ali  and
      Mohamed, Omar",
    editor = "Al-Khalifa, Hend  and
      Elsayed, Tamer  and
      Mubarak, Hamdy  and
      Al-Thubaity, Abdulmohsen  and
      Magdy, Walid  and
      Darwish, Kareem",
    booktitle = "Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.osact-1.12/",
    pages = "104--111",
    abstract = "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 5th 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."
}