Infusing NLU into Automatic Question Generation
Karen Mazidi, Paul Tarau · 2016
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.