论文标题
DTW在Qur'an QA 2022：使用变形金刚的转移学习来回答低资源域中
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain
论文作者
论文摘要
机器阅读理解（MRC）的任务是评估机器自然语言理解的有用基准。它在自然语言处理（NLP）字段中广受欢迎，这主要是由于许多语言发布的数据集。但是，MRC的研究已在包括宗教文本在内的几个领域进行了研究。古兰经2022共享任务的目标是通过对古兰经的回答和阅读理解研究来填补这一空白。本文介绍了古兰经QA 2022共享任务的DTW条目。我们的方法使用转移学习来利用可用的阿拉伯MRC数据。我们使用各种整体学习策略进一步改善了结果。我们的方法在测试集中提供了部分相互等级（PRR）的分数为0.49，证明了其在任务上的出色表现。
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.
