October 01, 2020 – /,
The thesis context is educational automatic question generation where our research group tries to automatically pose questions about arbitrary textbooks. Yet, state-of-the-art question generators expect the answer to the question they should generate as part of the input.
In paragraph: "... The difference is fluid friction, both within the fluid itself and between the fluid and its surroundings, which we call viscosity. ..."
In answer: "viscosity"
Out question: "How do we call the difference in fluid friction?"
The main goal of the thesis is therefore to extract such answer candidates (e.g. "viscosity“) from a given paragraph of text. This task has already been addressed with supervised and unsupervised models on a variety of datasets (e.g. SQuAD) which can be seen as a starting point and as a strong baseline. Finally, the thesis may also investigate how transferable the developed approaches are to other educational datasets.
Willis, A., Davis, G., Ruan, S., Manoharan, L., Landay, J., & Brunskill, E. (2019, June). Key Phrase Extraction for Generating Educational Question-Answer Pairs. In Proceedings of the Sixth (2019) ACM Conference on Learning@ Scale (pp. 1-10).
Keywords: NLU, information extraction