@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",
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{\%} $\rightarrow$ 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.",
}
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<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% \rightarrow 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.</abstract>
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%0 Conference Proceedings
%T A Feasibility Study of Answer-Agnostic Question Generation for Education
%A Dugan, Liam
%A Miltsakaki, Eleni
%A Upadhyay, Shriyash
%A Ginsberg, Etan
%A Gonzalez, Hannah
%A Choi, DaHyeon
%A Yuan, Chuning
%A Callison-Burch, Chris
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F dugan-etal-2022-feasibility
%X 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% \rightarrow 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.
%R 10.18653/v1/2022.findings-acl.151
%U https://aclanthology.org/2022.findings-acl.151
%U https://doi.org/10.18653/v1/2022.findings-acl.151
%P 1919-1926
Markdown (Informal)
[A Feasibility Study of Answer-Agnostic Question Generation for Education](https://aclanthology.org/2022.findings-acl.151) (Dugan et al., Findings 2022)
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.