DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain

TL;DR

This paper presents a transfer learning approach with ensemble strategies for question answering on Qur'an texts, achieving competitive results in a low-resource domain of religious texts.

Contribution

It introduces a novel transfer learning methodology combined with ensemble techniques for Qur'an QA, addressing the challenge of limited domain-specific data.

Findings

Achieved a partial Reciprocal Rank score of 0.49 on the test set.

Demonstrated the effectiveness of transfer learning in low-resource religious text domains.

Showed that ensemble strategies improve QA performance.

Abstract

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.