Proceedings

OSACT5 Proceedings

You can find the entire OSACT5 proceedings (including Qur'an QA 2022 papers) here.

Qur'an QA 2022 Papers

• Rana Malhas, Watheq Mansour and Tamer Elsayed. Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an

• Damith Dola Mullage Premasiri, Tharindu Ranasinghe, Wajdi Zaghouani and Ruslan Mitkov. DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain

• Esha Aftab and Muhammad Kamran Malik. eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers

• Ali Mostafa and Omar Mohamed. GOF at Qur'an QA 2022: Towards an Efficient Question Answering For The Holy Qu'ran In The Arabic Language Using Deep Learning-Based Approach

• Youssef MELLAH, Ibtissam Touahri, Zakaria Kaddari, Zakaria Haja, Jamal Berrich and Toumi Bouchentouf. LARSA22 at Qur’an QA 2022: Text-to-Text Transformer for Finding Answers to Questions from Qur’an

• Abdullah Alsaleh, Saud Althabiti, Ibtisam K. Alshammari, Sarah Alnefaie, Sanaa Alowaidi, Alaa Fahad Alsaqer, Eric Atwell, Abdulrahman Altahhan and Mohammad Ammar Alsalka. LK2022 at Qur'an QA 2022: Simple Transformers Model for Finding Answers to Questions from Qur'an

• Nikhil Singh. niksss at Qur'an QA 2022: A Heavily Optimized BERT Based Model for Answering Questions from the Holy Qu'ran

• Basem H.A. Ahmed, Motaz Saad and Eshrag A. Refaee. QQATeam at Qur’an QA 2022: Fine-Tunning Arabic QA Models for Qur’an QA Task

• Amr Keleg and Walid Magdy. SMASH at Qur’an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering Scenarios

• Ahmed Wasfey Sleem, Eman Mohammed lotfy Elrefai, Marwa Mohammed Matar and Haq Nawaz. Stars at Qur'an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur'an with Transformer Models and Releasing a New Dataset

• Mohamemd Alaa Elkomy and Amany M. Sarhan. TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models