Infusing NLU into Automatic Question Generation

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This paper presents an approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems.

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10 p.

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Mazidi, Karen & Tarau, Paul September 2016.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 24 times . More information about this paper can be viewed below.

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Description

This paper presents an approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems.

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10 p.

Notes

Abstract: We present a fresh approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems. In our evaluation of the top 20 questions, our system generated 71% more acceptable questions by informing the generation process with Natural Language Understanding techniques. The system also introduces our DeconStructure algorithm which creates an intuitive and practical structure for easily accessing sentence functional constituents in NLP applications.

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  • 9th International Natural Language Generation conference, September 5-8, 2016. Edinburgh, UK.

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  • Publication Title: Proceedings of The 9th International Natural Language Generation conference
  • Pages: 51-60
  • Peer Reviewed: Yes

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UNT Scholarly Works

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  • September 2016

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  • Sept. 17, 2017, 6:24 p.m.

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Mazidi, Karen & Tarau, Paul. Infusing NLU into Automatic Question Generation, paper, September 2016; Stroudsburg, Pennsylvania. (digital.library.unt.edu/ark:/67531/metadc993372/: accessed August 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.