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.
CITATION STYLE
Mazidi, K., & Tarau, P. (2016). Automatic question generation: From NLU to NLG. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9684, pp. 23–33). Springer Verlag. https://doi.org/10.1007/978-3-319-39583-8_3
Mendeley helps you to discover research relevant for your work.