A survey on text generation using generative adversarial networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorde Rosa, Gustavo H.-
Autor(es): dc.creatorPapa, João P.-
Data de aceite: dc.date.accessioned2025-08-21T17:36:51Z-
Data de disponibilização: dc.date.available2025-08-21T17:36:51Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2021.108098-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229013-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229013-
Descrição: dc.descriptionThis work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called “natural” language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.-
Descrição: dc.descriptionDepartment of Computing São Paulo State University Bauru-
Descrição: dc.descriptionDepartment of Computing São Paulo State University Bauru-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectGenerative adversarial Networks-
Palavras-chave: dc.subjectLanguage modeling-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectNatural language processing-
Palavras-chave: dc.subjectText generation-
Título: dc.titleA survey on text generation using generative adversarial networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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