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.
BibTeX
@inproceedings{dugan-etal-2022-feasibility,
title = "A Feasibility Study of Answer-Agnostic Question Generation for Education",
author = "Dugan, Liam and
Miltsakaki, Eleni and
Upadhyay, Shriyash and
Ginsberg, Etan and
Gonzalez, Hannah and
Choi, DaHyeon and
Yuan, Chuning and
Callison-Burch, Chris",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.151/",
doi = "10.18653/v1/2022.findings-acl.151",
pages = "1919--1926"
}