eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers

Esha Aftab, Muhammad Kamran Malik

Abstract
Question Answering (QA) has enticed the interest of NLP community in recent years. NLP enthusiasts are engineering new Models and fine-tuning the existing ones that can give out answers for the posed questions. The deep neural network models are found to perform exceptionally on QA tasks, but these models are also data intensive. For instance, BERT has outperformed many of its contemporary contenders on SQuAD dataset. In this work, we attempt at solving the closed domain reading comprehension Question Answering task on QRCD (Qur’anic Reading Comprehension Dataset) to extract an answer span from the provided passage, using BERT as a baseline model. We improved the model’s output by applying regularization techniques like weight-decay and data augmentation. Using different strategies we had 0.59% and 0.31% partial Reciprocal Ranking (pRR) on development and testing data splits respectively.
Anthology ID:
2022.osact-1.11
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:
96–103
Language:
URL:
https://aclanthology.org/2022.osact-1.11
DOI:
Bibkey:
Cite (ACL):
Esha Aftab and Muhammad Kamran Malik. 2022. eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers. 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 96–103, Marseille, France. European Language Resources Association.
Cite (Informal):
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers (Aftab & Malik, OSACT 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2022.osact-1.11.pdf
Data
ARCD, SQuAD, TyDiQA-GoldP
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