The task of machine reading comprehension (MRC) is a useful benchmark to
evaluate the natural language understanding of machines. It has gained
popularity in the natural language processing (NLP) field mainly due to the
large number of datasets released for many languages. However, the research in
MRC has been understudied in several domains, including religious texts. The
goal of the Qur'an QA 2022 shared task is to fill this gap by producing
state-of-the-art question answering and reading comprehension research on
Qur'an. This paper describes the DTW entry to the Quran QA 2022 shared task.
Our methodology uses transfer learning to take advantage of available Arabic
MRC data. We further improve the results using various ensemble learning
strategies. Our approach provided a partial Reciprocal Rank (pRR) score of 0.49
on the test set, proving its strong performance on the task.

本研究通过迁移学习和集成学习等策略，利用已有的阿拉伯语 MRC 数据，实现了对 Qur'an QA 2022 共享任务的有效问答和阅读理解，pRR 评分为 0.49。