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

Ahmed Sleem, Eman Mohammed lotfy Elrefai, Marwa Mohammed Matar, Haq Nawaz


Abstract
The Holy Qur’an is the most sacred book for more than 1.9 billion Muslims worldwide, and it provides a guide for their behaviours and daily interactions. Its miraculous eloquence and the divine essence of its verses (Khorami, 2014)(Elhindi,2017) make it far more difficult for non-scholars to answer their questions from the Qur’an. Here comes the significant role of technology in assisting all Muslims in answering their Qur’anic questions with state-of-the-art advancements in natural language processing (NLP) and information retrieval (IR). The task of constructing the finest automatic extractive Question Answering system from the Holy Qur’an with the use of the recently available Qur’anic Reading Comprehension Dataset(QRCD) was announced for LREC 2022 (Malhas et al., 2022) which opened up this new area for researchers around the world. In this paper, we propose a novel Qur’an Question Answering dataset with over 700 samples to aid future Qur’an research projects and three different approaches where we utilised self-attention based deep learning models (transformers) for building reliable intelligent question-answering systems for the Holy Qur’an that achieved a partial Reciprocal Rank (pRR) best score of 52% on the released QRCD test se
Anthology ID:
2022.osact-1.18
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:
146–153
Language:
URL:
https://aclanthology.org/2022.osact-1.18
DOI:
Bibkey:
Cite (ACL):
Ahmed Sleem, Eman Mohammed lotfy Elrefai, Marwa Mohammed Matar, and Haq Nawaz. 2022. 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. 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 146–153, Marseille, France. European Language Resources Association.
Cite (Informal):
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 (Sleem et al., OSACT 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.osact-1.18.pdf
Data
ARCD