dc.contributor.author | Kumar, Dhruv | |
dc.date.accessioned | 2020-07-28 18:59:00 (GMT) | |
dc.date.available | 2020-07-28 18:59:00 (GMT) | |
dc.date.issued | 2020-07-28 | |
dc.date.submitted | 2020-07-24 | |
dc.identifier.uri | http://hdl.handle.net/10012/16084 | |
dc.description.abstract | We present a new iterative approach towards unsupervised edit-based sentence simplification. Our approach is guided by a scoring function to select simplified sentences generated after iteratively performing word and phrase-level edits on the complex sentence. The scoring function measures different aspects of simplification: fluency, simplicity, and preservation of meaning. As a result, unlike past approaches, our method is controllable and interpretable and does not require a parallel training set since it is unsupervised. At the same time, using the Newsela and WikiLarge datasets, we experimentally show that our solution is nearly as effective as state-of-the-art supervised approaches. | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.relation.uri | https://github.com/ddhruvkr/Edit-Unsup-TS | en |
dc.subject | Natural Language Processing | en |
dc.subject | Machine Learning | en |
dc.subject | Text Simplification | en |
dc.title | Iterative Edit-based Unsupervised Sentence Simplification | en |
dc.type | Master Thesis | en |
dc.pending | false | |
uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
uws-etd.degree.discipline | Computer Science | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws-etd.degree | Master of Mathematics | en |
uws.contributor.advisor | Golab, Lukasz | |
uws.contributor.affiliation1 | Faculty of Mathematics | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |