@inproceedings{mahmoudi-etal-2023-gym,
    title = "{GYM} at Qur`an {QA} 2023 Shared Task: Multi-Task Transfer Learning for {Q}uranic Passage Retrieval and Question Answering with Large Language Models",
    author = "Mahmoudi, Ghazaleh  and
      Morshedzadeh, Yeganeh  and
      Eetemadi, Sauleh",
    editor = "Sawaf, Hassan  and
      El-Beltagy, Samhaa  and
      Zaghouani, Wajdi  and
      Magdy, Walid  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Habash, Nizar  and
      Khalifa, Salam  and
      Keleg, Amr  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      Mrini, Khalil  and
      Almatham, Rawan",
    booktitle = "Proceedings of ArabicNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.arabicnlp-1.79/",
    doi = "10.18653/v1/2023.arabicnlp-1.79",
    pages = "714--719",
    abstract = "This work addresses the challenges of question answering for vintage texts like the Quran. It introduces two tasks: passage retrieval and reading comprehension. For passage retrieval, it employs unsupervised fine-tuning sentence encoders and supervised multi-task learning. In reading comprehension, it fine-tunes an Electra-based model, demonstrating significant improvements over baseline models. Our best AraElectra model achieves 46.1{\%} partial Average Precision (pAP) on the unseen test set, outperforming the baseline by 23{\%}."
}