International Conference on Intelligent Tutoring Systems

ITS 2016: Intelligent Tutoring Systems pp 23-33

Automatic Question Generation: From NLU to NLG

Conference paper

DOI: 10.1007/978-3-319-39583-8_3

Volume 9684 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
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

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.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of North TexasDentonUSA