Produção Nacional
Revisado por pares
A survey on text generation using generative adversarial networks
2021; Elsevier BV; Volume: 119; Linguagem: Inglês
10.1016/j.patcog.2021.108098
ISSN1873-5142
AutoresGustavo Henrique de Rosa, João Paulo Papa,
Tópico(s)Natural Language Processing Techniques
ResumoThis 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.
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