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Automatic Question Generation: From NLU to NLG

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Intelligent Tutoring Systems (ITS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9684))

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Abstract

Questioning has been shown to improve learning outcomes, and automatic question generation can greatly facilitate the inclusion of questions in learning technologies such as intelligent tutoring systems. The majority of prior QG systems use parsing software and transformation algorithms to create questions. In contrast, the approach described here infuses natural language understanding (NLU) into the natural language generation (NLG) process by first analyzing the central semantic content of each independent clause in each sentence. Then question templates are matched to what the sentence is communicating in order to generate higher quality questions. This approach generated a higher percentage of acceptable questions than prior state-of-the-art systems.

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Notes

  1. 1.

    http://www.karenmazidi.com/.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of North Texas, Denton, TX, 76207, USA

    Karen Mazidi & Paul Tarau

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  1. Karen Mazidi
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  2. Paul Tarau
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Correspondence to Karen Mazidi .

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Editors and Affiliations

  1. Roma Tre University , Rome, Italy

    Alessandro Micarelli

  2. Carnegie Mellon University , Pittsburgh, Pennsylvania, USA

    John Stamper

  3. Neoanalysis Ltd , Athens, Greece

    Kitty Panourgia

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Mazidi, K., Tarau, P. (2016). Automatic Question Generation: From NLU to NLG. In: Micarelli, A., Stamper, J., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science(), vol 9684. Springer, Cham. https://doi.org/10.1007/978-3-319-39583-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-39583-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39582-1

  • Online ISBN: 978-3-319-39583-8

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