An adversarial model for paraphrase generation

• Autor: Vizcarra Aguilar, Gerson Waldyr
• Formato: tesis de maestría
• Fecha de Publicación: 2020
• Institución: Universidad Católica San Pablo
• Repositorio: UCSP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ucsp.edu.pe:20.500.12590/16901
• Enlace del recurso: https://hdl.handle.net/20.500.12590/16901
• Nivel de acceso: acceso abierto
• Materia: Paraphrase generation; Input representations; Convolutional sequence to sequence; Adversarial training; https://purl.org/pe-repo/ocde/ford#1.02.01

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 difficult 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.