Zhao, Sanqiang
(2021)
Constructing Tunable Sentence Simplification Models using Deep Learning.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning so that certain individuals can read and understand it. Substitution, Dropping, Reordering, and Splitting are widely accepted as four important operations. Recent approaches view the simplification process as a monolingual text-to-text translation, where the translation model learns the operations automatically from examples of complex-simplified sentence pairs extracted from online resources. In the current literature, the two publicly available resources commonly used are Wikipedia and Newsela. However, both resources are limited in several ways, and only contribute to certain operations.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
15 April 2021 |
| Date Type: |
Publication |
| Defense Date: |
23 March 2021 |
| Approval Date: |
7 June 2021 |
| Submission Date: |
26 April 2021 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
144 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Computing and Information > Information Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
No |
| Uncontrolled Keywords: |
sentence simplification, controllable generation |
| Date Deposited: |
07 Jun 2021 20:49 |
| Last Modified: |
11 Oct 2024 18:57 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/40638 |
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