DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
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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 repeated text and high reward for "novel" 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 method can generate substantially more diverse and informative text than existing baseline methods. The code is available at https://github.com/lancopku/DPGAN.
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