Text generation with generative adversarial networks (GANs) can be divided into the text-based and code-based categories according to the type of signals used for discrimination. In this work, we introduce a novel text-based approach called Soft-GAN to effectively exploit GAN setup for text generation. We demonstrate how autoencoders (AEs) can be used for providing a continuous representation of sentences, which we will refer to as soft-text. This soft representation will be used in GAN discrimination to synthesize similar soft-texts. We also propose hybrid latent code and text-based GAN (LATEXT-GAN) approaches with one or more discriminators, in which a combination of the latent code and the soft-text is used for GAN discriminations. We perform a number of subjective and objective experiments on two well-known datasets (SNLI and Image COCO) to validate our techniques. We discuss the results using several evaluation metrics and show that the proposed techniques outperform the traditional GAN-based text-generation methods.

介绍了一种新型的基于文本的生成对抗网络 Soft-GAN，并提出了将自编码器用于提供连续句子表示的方法，称为 soft-text。同时还提出了混合潜在编码和文本的 GAN 方法并在两个著名数据集上进行了验证实验，结果显示这些方法优于传统的基于 GAN 的文本生成方法。

基于潜在代码和文本的生成对抗网络用于软文本生成