| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Chen, G. | |
| dc.contributor.author | Wang, Ziyuan | |
| dc.date.accessioned | 2023-09-30T00:00:49Z | |
| dc.date.available | 2023-09-30T00:00:49Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/45270 | |
| dc.description.abstract | With the development of Artificial Intelligence, style transformation has made progress in computer vision and natural language processing. Style transfer originated in the field of vision and has been extended to the field of text, defined as preserving the content while changing the style of the text, for example, attributes such as politeness, formality, and humor. However, for one of the popular sentiment style transfer tasks, we argue that it should not belong to style transfer. We selected | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | With the development of Artificial Intelligence, style transformation has made progress in computer vision and natural language processing. Style transfer originated in the field of vision and has been extended to the field of text, defined as preserving the content while changing the style of the text, for example, attributes such as politeness, formality, and humor. However, for one of the popular sentiment style transfer tasks, we argue that it should not belong to style transfer. We selected | |
| dc.title | Assessing Sentiment Transfer Models on “Real” Text Style Transfer Tasks | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.keywords | NLP, style transfer, text style transfer | |
| dc.subject.courseuu | Computing Science | |
| dc.thesis.id | 24875 | |
