  In recent years, we witnessed great progress in different tasks of natural
language understanding using machine learning. Question answering is one of
these tasks which is used by search engines and social media platforms for
improved user experience. Arabic is the language of the Holy Qur'an; the sacred
text for 1.8 billion people across the world. Arabic is a challenging language
for Natural Language Processing (NLP) due to its complex structures. In this
article, we describe our attempts at OSACT5 Qur'an QA 2022 Shared Task, which
is a question answering challenge on the Holy Qur'an in Arabic. We propose an
ensemble learning model based on Arabic variants of BERT models. In addition,
we perform post-processing to enhance the model predictions. Our system
achieves a Partial Reciprocal Rank (pRR) score of 56.6% on the official test
set.
