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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12590/16901
Title: An adversarial model for paraphrase generation
Authors: Vizcarra Aguilar, Gerson Waldyr
metadata.dc.contributor.advisor: Ochoa Luna, Jose Eduardo
Keywords: Paraphrase generation;Input representations;Convolutional sequence to sequence;Adversarial training
Issue Date: 2020
Publisher: Universidad Católica San Pablo
Abstract: Paraphrasing is the action of expressing the idea of a sentence using different words. Paraphrase generation is an interesting and challenging task due mainly to three reasons: (1) The nature of the text is discrete, (2) it is diffcult to modify a sentence slightly without changing the meaning, and (3) there are no accurate automatic metrics to evaluate the quality of a paraphrase. This problem has been addressed with several methods. Even so, neural network-based approaches have been tackling this task recently. This thesis presents a novel framework to solve the paraphrase generation problem in English. To do so, this work focuses and evaluates three aspects of a model, as the teaser figure shows. (a) Static input representations extracted from pre-trained language models. (b) Convolutional sequence to sequence models as our main architecture. (c) Hybrid loss function between maximum likelihood and adversarial REINFORCE, avoiding the computationally expensive Monte-Carlo search. We compare our best models with some baselines in the Quora question pairs dataset. The results show that our framework is competitive against the previous benchmarks.
URI: http://hdl.handle.net/20.500.12590/16901
Appears in Collections:Tesis Postgrado - Maestría en Ciencia de la Computación

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