A Feasibility Study of Answer-Agnostic Question Generation for Education

Liam Dugan, Eleni Miltsakaki, Shriyash Upadhyay, Etan Ginsberg, Hannah Gonzalez, DaHyeon Choi, Chuning Yuan, Chris Callison-Burch


Abstract
We conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages. We show that a significant portion of errors in such systems arise from asking irrelevant or un-interpretable questions and that such errors can be ameliorated by providing summarized input. We find that giving these models human-written summaries instead of the original text results in a significant increase in acceptability of generated questions (33% 83%) as determined by expert annotators. We also find that, in the absence of human-written summaries, automatic summarization can serve as a good middle ground.
Anthology ID:
2022.findings-acl.151
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1919–1926
Language:
URL:
https://aclanthology.org/2022.findings-acl.151
DOI:
10.18653/v1/2022.findings-acl.151
Bibkey:
Cite (ACL):
Liam Dugan, Eleni Miltsakaki, Shriyash Upadhyay, Etan Ginsberg, Hannah Gonzalez, DaHyeon Choi, Chuning Yuan, and Chris Callison-Burch. 2022. A Feasibility Study of Answer-Agnostic Question Generation for Education. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1919–1926, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
A Feasibility Study of Answer-Agnostic Question Generation for Education (Dugan et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.151.pdf
Code
 liamdugan/summary-qg