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- ---
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- license: cc-by-nc-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-nc-4.0
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+ ---
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+ # ArQuAD: An Expert-Annotated Arabic Machine Reading Comprehension Dataset
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+
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+ ## Overview
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+
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+ ArQuAD is an expert-annotated Arabic Machine Reading Comprehension (MRC) dataset. It comprises 16,020 questions posed by language experts on passages extracted from the most frequently visited Arabic Wikipedia articles. Each question's answer is a text segment from the corresponding reading passage.
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+
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+ ## Citation
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+ If you use ArQuAD in your research, please cite the following paper:
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+ bibtex
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+ @article{obeidat2024arquad,
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+ title={ArQuAD: An Expert-Annotated Arabic Machine Reading Comprehension Dataset},
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+ author={Obeidat, Rasha and Al-Harbi, Marwa and Al-Ayyoub, Mahmoud and Alawneh, Luay},
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+ journal={Cognitive Computation},
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+ pages={1--20},
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+ year={2024},
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+ publisher={Springer}
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+ }
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+ ## Dataset Description
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+
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+ ArQuAD consists of 16,020 question-answer pairs created by Arabic language specialists with BA and MA degrees. The passages are sourced from 1335 of the most viewed Arabic Wikipedia articles, covering a wide range of topics including sports, politics, technology, religion, and more.
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+
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+ ### Structure
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+ The dataset is provided in both CSV and SQuAD JSON formats with the following columns:
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+ - `passage`: The original passage from Wikipedia.
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+ - `question`: The question posed by the annotator.
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+ - `answer`: The minimal text span from the passage that answers the question.
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+
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+ ### Statistics
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+ The dataset includes:
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+ - Total pairs: 16,020
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+ - Passages: 4,005
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+ - Domains covered: Various (sports, politics, technology, etc.)
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+
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+ ### Key Features
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+ - **Expert-Annotated**: Questions and answers are created by language experts, ensuring high quality and relevance.
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+ - **Diverse**: Covers a wide range of topics to ensure comprehensive testing of MRC models, also includes a mix of factoid and non-factoid questions.
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+
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+ ## Usage
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+ Download the CSV file from the repository to use this dataset and load it into your preferred data analysis tool.
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+
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+ ## Contact
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+ or any questions or issues regarding the dataset, please contact:
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+ Rasha Obeidat:rmobeidat@just.edu.jo (or any of the authors)
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