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Applying generative adversarial networks (GANs) to text-related tasks ischallenging due to the discrete nature of language. One line of researchresolves this issue by employing reinforcement learning (RL) and optimizing thenext-word sampling policy directly in a discrete action space. Such methodscompute the rewards from complete sentences and avoid error accumulation due toexposure bias. Other approaches employ approximation techniques that map thetext to continuous representation in order to circumvent the non-differentiablediscrete process. Particularly, autoencoder-based methods effectively producerobust representations that can model complex discrete structures. In thispaper, we propose a novel text GAN architecture that promotes the collaborativetraining of the continuous-space and discrete-space methods. Our method employsan autoencoder to learn an implicit data manifold, providing a learningobjective for adversarial training in a continuous space. Furthermore, thecomplete textual output is directly evaluated and updated via RL in a discretespace. The collaborative interplay between the two adversarial trainingseffectively regularize the text representations in different spaces. Theexperimental results on three standard benchmark datasets show that our modelsubstantially outperforms state-of-the-art text GANs with respect to quality,diversity, and global consistency.

Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation