@inproceedings{singh-2022-niksss-quran,
    title = "niksss at Qur`an {QA} 2022: A Heavily Optimized {BERT} Based Model for Answering Questions from the Holy Qu`ran",
    author = "Singh, Nikhil",
    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/jlcl-multiple-ingestion/2022.osact-1.15/",
    pages = "126--129",
    abstract = "This paper presents the system description by team niksss for the Qur`an QA 2022 Shared Task. The goal of this shared task was to evaluate systems for Arabic Reading Comprehension over the Holy Quran. The task was set up as a question-answering task, such that, given a passage from the Holy Quran (consisting of consecutive verses in a specific surah(Chapter)) and a question (posed in Modern Standard Arabic (MSA)) over that passage, the system is required to extract a span of text from that passage as an answer to the question. The span was required to be an exact sub-string of the passage. We attempted to solve this task using three techniques namely conditional text-to-text generation, embedding clustering, and transformers-based question answering."
}