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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.findings-acl.151.pdf
- Code
- liamdugan/summary-qg