Neural Interface for Web-Scale Knowledge
| dc.contributor.advisor | Hajishirzi, Hannaneh | |
| dc.contributor.advisor | Farhadi, Ali | |
| dc.contributor.author | Seo, Minjoon | |
| dc.date.accessioned | 2020-08-14T03:28:32Z | |
| dc.date.available | 2020-08-14T03:28:32Z | |
| dc.date.issued | 2020-08-14 | |
| dc.date.submitted | 2020 | |
| dc.description | Thesis (Ph.D.)--University of Washington, 2020 | |
| dc.description.abstract | Modern 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.terms | Open Access | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.other | Seo_washington_0250E_21539.pdf | |
| dc.identifier.uri | http://hdl.handle.net/1773/45926 | |
| dc.language.iso | en_US | |
| dc.rights | none | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Deep Learning | |
| dc.subject | Machine Learning | |
| dc.subject | Natural Language Processing | |
| dc.subject | Computer science | |
| dc.subject.other | Computer science and engineering | |
| dc.title | Neural Interface for Web-Scale Knowledge | |
| dc.type | Thesis |
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