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Remember the facts? Investigating Answer-aware Neural Question Generation for Text Comprehension

Author:Tim Steuer, Anna Filighera, Christoph Rensing
Date:July 2020
Kind:In proceedings - use for conference & workshop papers
Publisher:Springer International Publishing
Book title:Artificial Intelligence in Education
Editor:41 Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (Eds.)
Research Area(s):Knowledge Media
Abstract:Reading is a crucial skill in the 21st century. Thus, scaffolding text comprehension by automatically generated questions may greatly profit learners. Yet, the state-of-the-art methods for automatic question generation, answer-aware neural question generators (NQGs), are rarely seen in the educational domain. Hence, we investigate the quality of questions generated by a novel approach comprising an answer- aware NQG and two novel answer candidate selection strategies based on semantic graph matching. In median, the approach generates clear, answerable and useful factual questions outperforming an answer-unaware NQG on educational datasets as shown by automatic and human evalua- tion. Furthermore, we analyze the types of questions generated, showing that the question types differ across answer selection strategies yet re- main factual.
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