  Existing text generation methods tend to produce repeated and "boring"
expressions. To tackle this problem, we propose a new text generation model,
called Diversity-Promoting Generative Adversarial Network (DP-GAN). The
proposed model assigns low reward for repeatedly generated text and high reward
for "novel" and fluent text, encouraging the generator to produce diverse and
informative text. Moreover, we propose a novel language-model based
discriminator, which can better distinguish novel text from repeated text
without the saturation problem compared with existing classifier-based
discriminators. The experimental results on review generation and dialogue
generation tasks demonstrate that our model can generate substantially more
diverse and informative text than existing baselines. The code is available at
https://github.com/lancopku/DPGAN
