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.identifier.uri |
https://scholarsbank.uoregon.edu/xmlui/handle/1794/27594 |
|
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.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.name |
M.S. |
|
thesis.degree.level |
masters |
|
thesis.degree.discipline |
Department of Computer and Information Science |
|
thesis.degree.grantor |
University of Oregon |
|