  Generative Adversarial Networks (GANs) have known a tremendous success for
many continuous generation tasks, especially in the field of image generation.
However, for discrete outputs such as language, optimizing GANs remains an open
problem with many instabilities, as no gradient can be properly back-propagated
from the discriminator output to the generator parameters. An alternative is to
learn the generator network via reinforcement learning, using the discriminator
signal as a reward, but such a technique suffers from moving rewards and
vanishing gradient problems. Finally, it often falls short compared to direct
maximum-likelihood approaches. In this paper, we introduce Generative
Cooperative Networks, in which the discriminator architecture is cooperatively
used along with the generation policy to output samples of realistic texts for
the task at hand. We give theoretical guarantees of convergence for our
approach, and study various efficient decoding schemes to empirically achieve
state-of-the-art results in two main NLG tasks.
