Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an

Rana Malhas, Watheq Mansour, Tamer Elsayed


Abstract
Motivated by the resurgence of the machine reading comprehension (MRC) research, we have organized the first Qur’an Question Answering shared task, “Qur’an QA 2022”. The task in its first year aims to promote state-of-the-art research on Arabic QA in general and MRC in particular on the Holy Qur’an, which constitutes a rich and fertile source of knowledge for Muslim and non-Muslim inquisitors and knowledge-seekers. In this paper, we provide an overview of the shared task that succeeded in attracting 13 teams to participate in the final phase, with a total of 30 submitted runs. Moreover, we outline the main approaches adopted by the participating teams in the context of highlighting some of our perceptions and general trends that characterize the participating systems and their submitted runs.
Anthology ID:
2022.osact-1.9
Volume:
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:
June
Year:
2022
Address:
Marseille, France
Editors:
Hend Al-Khalifa, Tamer Elsayed, Hamdy Mubarak, Abdulmohsen Al-Thubaity, Walid Magdy, Kareem Darwish
Venue:
OSACT
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
79–87
Language:
URL:
https://aclanthology.org/2022.osact-1.9
DOI:
Bibkey:
Cite (ACL):
Rana Malhas, Watheq Mansour, and Tamer Elsayed. 2022. Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an. In 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, pages 79–87, Marseille, France. European Language Resources Association.
Cite (Informal):
Qur’an QA 2022: Overview of The First Shared Task on Question Answering over the Holy Qur’an (Malhas et al., OSACT 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.osact-1.9.pdf
Data
ARCD