mlsanityData to 14 Oct 2026

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

arXiv 2205.06025 ↗First posted 12 May 2022cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 12 May 2022.

Builds on

26 of 43 references.

References · 26

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14 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

4 papers.

  1. 2026From RAG to Agentic RAG for Faithful Islamic Question Answeringcited by 9
  2. 2022BERT(s) to Detect Multiword Expressionscited by 5
  3. 2022Transformer-based Detection of Multiword Expressions in Flower and Plant Namescited by 0
  4. 2023Deep Learning Methods for Extracting Metaphorical Names of Flowers and Plantscited by 0