Deep Context Resolution

dc.contributor.advisorLi, Ming
dc.contributor.authorChen, Junnan
dc.date.accessioned2018-05-17T13:23:27Z
dc.date.available2018-05-17T13:23:27Z
dc.date.issued2018-05-17
dc.date.submitted2018-05-11
dc.description.abstractConversations depend on information from the context. To go beyond one-round conversation, a chatbot must resolve contextual information such as: 1) co-reference resolution, 2) ellipsis resolution, and 3) conjunctive relationship resolution. There are simply not enough data to avoid these problems by trying to train a sequence-to-sequence model for multi-round conversation similar to that of one-round conversation. The contributions of this paper are: 1) We formulate the problem of context resolution for conversation; 2) We present deep learning models, including an end-to-end network for context resolution; 3) We propose a way of creating a huge amount ofen
dc.identifier.urihttp://hdl.handle.net/10012/13301
dc.language.isoenen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.subjectnlpen
dc.subjectcontext resolutionen
dc.subjectcoreferenceen
dc.subjectdeep learningen
dc.subjectdeep neural networksen
dc.subjectdialogen
dc.subjectconversation understandingen
dc.titleDeep Context Resolutionen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws.contributor.advisorLi, Ming
uws.contributor.affiliation1Faculty of Mathematicsen
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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