@inproceedings{aftab-malik-2022-erock,
    title = "e{R}ock at Qur{'}an {QA} 2022: Contemporary Deep Neural Networks for Qur{'}an based Reading Comprehension Question Answers",
    author = "Aftab, Esha  and
      Malik, Muhammad Kamran",
    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-1.11/",
    pages = "96--103",
    abstract = "Question Answering (QA) has enticed the interest of NLP community in recent years. NLP enthusiasts are engineering new Models and fine-tuning the existing ones that can give out answers for the posed questions. The deep neural network models are found to perform exceptionally on QA tasks, but these models are also data intensive. For instance, BERT has outperformed many of its contemporary contenders on SQuAD dataset. In this work, we attempt at solving the closed domain reading comprehension Question Answering task on QRCD (Qur{'}anic Reading Comprehension Dataset) to extract an answer span from the provided passage, using BERT as a baseline model. We improved the model{'}s output by applying regularization techniques like weight-decay and data augmentation. Using different strategies we had 0.59{\%} and 0.31{\%} partial Reciprocal Ranking (pRR) on development and testing data splits respectively."
}