Abstract
Asking questions about a situation is an inherent step towards understanding it. To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage. Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text. Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere. Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.
Original language | English |
---|---|
Title of host publication | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1429-1441 |
Number of pages | 13 |
ISBN (Electronic) | 9781955917094 |
State | Published - 2021 |
Event | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic Duration: 7 Nov 2021 → 11 Nov 2021 |
Publication series
Name | EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings |
---|
Conference
Conference | 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 |
---|---|
Country/Territory | Dominican Republic |
City | Virtual, Punta Cana |
Period | 7/11/21 → 11/11/21 |
Bibliographical note
Publisher Copyright:© 2021 Association for Computational Linguistics
Funding
We would like to thank Daniela Brook-Weiss for helping in the initial stages of this project and the anonymous reviewers for their insightful comments. The work described herein was supported in part by grants from Intel Labs, Facebook, the Israel Science Foundation grant 1951/17. This project has received funding from the Europoean Research Council (ERC) under the Europoean Union's Horizon 2020 research and innovation programme, grant agreements No. 802774 (iEXTRACT) and No. 677352 (NLPRO). We would like to thank Daniela Brook-Weiss for helping in the initial stages of this project and the anonymous reviewers for their insightful comments. The work described herein was supported in part by grants from Intel Labs, Facebook, the Israel Science Foundation grant 1951/17. This project has received funding from the Europoean Research Council (ERC) under the Europoean Union’s Horizon 2020 research and innovation programme, grant agreements No. 802774 (iEXTRACT) and No. 677352 (NLPRO).
Funders | Funder number |
---|---|
Europoean Union's Horizon 2020 research and innovation programme | |
Europoean Union’s Horizon 2020 research and innovation programme | |
Intel Labs | |
Horizon 2020 Framework Programme | 802774, 677352 |
European Commission | |
Israel Science Foundation | 1951/17 |