CANARD: A dataset for Question-in-Context Rewriting
dc.contributor.advisor | Boyd-Graber, Jordan | |
dc.contributor.author | Ghoneim, Ahmed Elgohary | |
dc.contributor.author | Peskov, Denis | |
dc.date.accessioned | 2021-08-01T17:00:17Z | |
dc.date.available | 2021-08-01T17:00:17Z | |
dc.date.issued | 2019-11-03 | |
dc.description.abstract | In 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.sponsorship | This work was supported by NSF grant IIS-1822494. Boyd-Graber and Peskov were also supported by IIS-1748663. | en_US |
dc.description.uri | https://aclanthology.org/D19-1605 | |
dc.identifier | https://doi.org/10.13016/zcop-ns5d | |
dc.identifier.citation | Ahmed 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.uri | http://hdl.handle.net/1903/27595 | |
dc.language.iso | en | en_US |
dc.relation.isAvailableAt | College of Computer, Mathematical & Natural Sciences | en_us |
dc.relation.isAvailableAt | Computer Science | en_us |
dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
dc.relation.isAvailableAt | University of Maryland (College Park, MD) | en_us |
dc.subject | Question Answering | en_US |
dc.subject | Conversational Systems | en_US |
dc.subject | CANARD | en_US |
dc.title | CANARD: A dataset for Question-in-Context Rewriting | en_US |
dc.type | Dataset | en_US |
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