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Most of the existing generative adversarial networks (GAN) for textgeneration suffer from the instability of reinforcement learning trainingalgorithms such as policy gradient, leading to unstable performance. To tacklethis problem, we propose a novel framework called Adversarial Reward AugmentedMaximum Likelihood (ARAML). During adversarial training, the discriminatorassigns rewards to samples which are acquired from a stationary distributionnear the data rather than the generator's distribution. The generator isoptimized with maximum likelihood estimation augmented by the discriminator'srewards instead of policy gradient. Experiments show that our model canoutperform state-of-the-art text GANs with a more stable training process.

ARAML: A Stable Adversarial Training Framework for Text Generation