论文标题
使用生成对抗网络对文本生成的调查
A survey on text generation using generative adversarial networks
论文作者
论文摘要
这项工作对使用生成对抗网络的最新研究和文本生成进步进行了详尽的审查。对对抗性学习对文本生成的使用是有希望的，因为它提供了产生所谓的“自然”语言的替代方法。然而，对抗性文本生成并不是一个简单的任务，因为其最重要的架构（生成对抗网络）旨在应对连续信息（图像）而不是离散数据（文本）。因此，大多数作品基于三个可能的选项，即gumbel-softmax差异化，增强学习和修改的培训目标。在本调查中审查了所有替代方案，因为它们介绍了使用基于对抗的技术生成文本的最新方法。选定的作品取自著名的数据库，例如Science Direct，IEEXplore，Springer，计算机协会和ARXIV，而每项选定的作品都经过严格分析和评估，以呈现其目标，方法论和实验结果。
This 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.
