Neural Interface for Web-Scale Knowledge

dc.contributor.advisorHajishirzi, Hannaneh
dc.contributor.advisorFarhadi, Ali
dc.contributor.authorSeo, Minjoon
dc.date.accessioned2020-08-14T03:28:32Z
dc.date.available2020-08-14T03:28:32Z
dc.date.issued2020-08-14
dc.date.submitted2020
dc.descriptionThesis (Ph.D.)--University of Washington, 2020
dc.description.abstractModern natural language tasks are increasingly dependent on external world knowledge. My PhD study has particularly focused on three challenges in this literature: making sense of unstructured knowledge, leveraging extremely large knowledge, and reasoning over the knowledge data. I will mainly discuss my approaches to tackle these challenges and how they can serve as an effective interface for interacting with the world knowledge. I will conclude with an argument that designing a seamless and universal knowledge interface is a crucial research goal that can better address knowledge-dependency problem in machine learning tasks.
dc.embargo.termsOpen Access
dc.format.mimetypeapplication/pdf
dc.identifier.otherSeo_washington_0250E_21539.pdf
dc.identifier.urihttp://hdl.handle.net/1773/45926
dc.language.isoen_US
dc.rightsnone
dc.subjectArtificial Intelligence
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectNatural Language Processing
dc.subjectComputer science
dc.subject.otherComputer science and engineering
dc.titleNeural Interface for Web-Scale Knowledge
dc.typeThesis

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