Asking It All: Generating Contextualized Questions for any Semantic Role

Valentina Pyatkin, Paul Roit, Julian Michael, Reut Tsarfaty, Yoav Goldberg, Ido Dagan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

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 languageEnglish
Title of host publicationEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages1429-1441
Number of pages13
ISBN (Electronic)9781955917094
StatePublished - 2021
Event2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021

Publication series

NameEMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
Country/TerritoryDominican Republic
CityVirtual, Punta Cana
Period7/11/2111/11/21

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).

FundersFunder number
Europoean Union's Horizon 2020 research and innovation programme
Europoean Union’s Horizon 2020 research and innovation programme
Intel Labs
Horizon 2020 Framework Programme802774, 677352
European Commission
Israel Science Foundation1951/17

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