  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.
