Crowdsourcing question-answer meaning representations

Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer

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

47 Scopus citations

Abstract

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A qualitative analysis demonstrates that the crowd-generated questionanswer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, Nom- Bank, and QA-SRL) along with many previously under-resourced ones, including implicit arguments and relations. We also report baseline models for question generation and answering, and summarize a recent approach for using QAMR labels to improve an Open IE system. These results suggest the freely available1 QAMR data and annotation scheme should support significant future work.

Original languageAmerican English
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages560-568
Number of pages9
ISBN (Electronic)9781948087292
StatePublished - 2018
Externally publishedYes
Event2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
Duration: 1 Jun 20186 Jun 2018

Publication series

NameNAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume2

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Country/TerritoryUnited States
CityNew Orleans
Period1/06/186/06/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computational Linguistics.

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