Exploring the Reformulation of NLP Tasks as Text Generation Tasks
| dc.contributor.advisor | Nguyen, Thien | |
| dc.contributor.author | Hasan, Rasti | |
| dc.date.accessioned | 2022-10-04T19:38:01Z | |
| dc.date.available | 2022-10-04T19:38:01Z | |
| dc.date.issued | 2022-10-04 | |
| dc.description.abstract | In recent years, NLP classification tasks have been reformulated as text generation tasks in the form of text-to-text transformer-based models that achieve state-of-the-art performance by better utilizing pre-trained language models. This work provides a historical background, a taxonomy based on the output structures of these methods, an exploration of aspects of such models with several representative works, and discusses the current state and future of these models. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/27594 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | All Rights Reserved. | |
| dc.subject | Generative Models | en_US |
| dc.subject | Natural Language Processing | en_US |
| dc.title | Exploring the Reformulation of NLP Tasks as Text Generation Tasks | |
| dc.type | Electronic Thesis or Dissertation | |
| thesis.degree.discipline | Department of Computer and Information Science | |
| thesis.degree.grantor | University of Oregon | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.S. |
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