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Remember the facts? Investigating Answer-aware Neural Question Generation for Text Comprehension
| Key: | SFR20 |
| 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.) |
| Number: | 21 |
| Pages: | 512-523 |
| ISBN: | 978-3-030-52237-7 |
| Language: | En |
| 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. |
| URL: | doi.org/10.1007/978-3-030-52237-7_41 |
| Full paper (pdf) | |
