An adversarial model for paraphrase generation

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dc.contributor.advisorOchoa Luna, Jose Eduardo
dc.contributor.authorVizcarra Aguilar, Gerson Waldyr
dc.date.accessioned2021-11-02T16:39:39Z
dc.date.available2021-11-02T16:39:39Z
dc.date.issued2020
dc.description.abstractParaphrasing 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. es_PE
dc.description.uriTesises_PE
dc.formatapplication/pdfes_PE
dc.identifier.other1073514
dc.identifier.urihttps://hdl.handle.net/20.500.12590/16901
dc.language.isoenges_PE
dc.publisherUniversidad Católica San Pabloes_PE
dc.publisher.countryPEes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectParaphrase generationes_PE
dc.subjectInput representationses_PE
dc.subjectConvolutional sequence to sequencees_PE
dc.subjectAdversarial traininges_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.01es_PE
dc.titleAn adversarial model for paraphrase generationes_PE
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
renati.advisor.dni29738760
renati.advisor.orcidhttps://orcid.org/0000-0002-8979-3785es_PE
renati.author.dni70001862
renati.discipline611017es_PE
renati.jurorAlex Jesús Cuadros Vargases_PE
renati.jurorEraldo Luíz Rezende Fernandeses_PE
renati.jurorCamilo Thorne Freundtes_PE
renati.jurorHugo Alatrista Salases_PE
renati.levelhttps://purl.org/pe-repo/renati/level#maestro
renati.typehttps://purl.org/pe-repo/renati/type#tesis
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computaciónes_PE
thesis.degree.levelMaestríaes_PE
thesis.degree.nameMaestro en Ciencia de la Computaciónes_PE
thesis.degree.programPrograma Profesional de Ciencia de la Computaciónes_PE
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