CANARD: A dataset for Question-in-Context Rewriting

dc.contributor.advisorBoyd-Graber, Jordan
dc.contributor.authorGhoneim, Ahmed Elgohary
dc.contributor.authorPeskov, Denis
dc.date.accessioned2021-08-01T17:00:17Z
dc.date.available2021-08-01T17:00:17Z
dc.date.issued2019-11-03
dc.description.abstractIn conversational question answering multiple questions in an information-seeking dialogs which requires models to link questions together to resolve the conversational dependencies between them: each question needs to be under- stood in the conversation context. For example, the question “What was he like in that episode?” cannot be understood without knowing what “he” and “that episode” refer to, which can be resolved using the conversation context. CANARD is a dataset of 40,000 questions asked in conversational contexts paired with their gold context-independent (stand-alone) rewrite.en_US
dc.description.sponsorshipThis work was supported by NSF grant IIS-1822494. Boyd-Graber and Peskov were also supported by IIS-1748663.en_US
dc.description.urihttps://aclanthology.org/D19-1605
dc.identifierhttps://doi.org/10.13016/zcop-ns5d
dc.identifier.citationAhmed Elgohary, Denis Peskov, and Jordan Boyd-Graber. Can You Unpack That? Learning to Rewrite Questions-in-Context. Empirical Methods in Natural Language Processing, 2019.en_US
dc.identifier.urihttp://hdl.handle.net/1903/27595
dc.language.isoenen_US
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtComputer Scienceen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectQuestion Answeringen_US
dc.subjectConversational Systemsen_US
dc.subjectCANARDen_US
dc.titleCANARD: A dataset for Question-in-Context Rewritingen_US
dc.typeDataseten_US

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