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A Comprehensive Review of Arabic Question Answering Datasets

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1961))

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Abstract

The research community has shown significant interest in Question Answering (QA) due to the strong relevance of QA applications. In recent years, there has been a significant increase in the availability of publicly accessible datasets aimed at advancing research in Arabic QA systems. This survey aims to identify, summarize, and analyze current Arabic QA datasets, such as Monolingual, Multilingual, and Cross-lingual. Our research surveys the existing datasets and provides a comprehensive and multi-faceted classification. Furthermore, this study aims to guide research in Arabic QA by providing the latest updates about the state-of-the-art in this field and identifying shortcomings in the current datasets to develop more substantial and improved collections. Finally, we discuss the existing challenges in Arabic QA datasets and highlight their potential benefits for future research.

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Notes

  1. 1.

    https://omarito.me/arabic-askfm-dataset/.

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Authors and Affiliations

  1. Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia

    Yassine Saoudi

  2. Higher Institute of Arts and Multimedia Manouba, University of Manouba, Manouba, Tunisia

    Mohamed Mohsen Gammoudi

Authors
  1. Yassine Saoudi
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  2. Mohamed Mohsen Gammoudi
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Correspondence to Yassine Saoudi .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Saoudi, Y., Gammoudi, M.M. (2024). A Comprehensive Review of Arabic Question Answering Datasets. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_22

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  • DOI: https://doi.org/10.1007/978-981-99-8126-7_22

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  • Online ISBN: 978-981-99-8126-7

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