Name
Abstract | ||
|---|---|---|
We conduct a feasibility study into the applicability of answer-unaware 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% |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/v1/2022.findings-acl.151 | Annual Meeting of the Association for Computational Linguistics |
DocType | Volume | Citations |
Conference | Findings of the Association for Computational Linguistics: ACL 2022 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liam Dugan | 1 | 0 | 0.34 |
| Eleni Miltsakaki | 2 | 0 | 0.34 |
| Shriyash Upadhyay | 3 | 0 | 0.34 |
| Etan Ginsberg | 4 | 0 | 0.34 |
| Hannah Gonzalez | 5 | 0 | 0.34 |
| Dayheon Choi | 6 | 0 | 0.34 |
| Chuning Yuan | 7 | 0 | 0.34 |
| Chris Callison-Burch | 8 | 4872 | 259.75 |