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Diversity Promoting GAN: A Cross Entropy Based Generative Adversarial Network for Diversified Text Generation

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Academic year: 2020

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Figure

Figure 1: Illustration of DP-GAN. Lower: The gener-ator is trained by policy gradient where the reward isprovided by the discriminator
Table 1: Performance of the DP-GAN and three base-lines on review generation and dialogue generationtasks
Table 2: Results of human evaluation on the threedatasets. The score represents the averaged ranking ofeach model and lower is better
Figure 2: Distribution of rewards between SeqGAN and DP-GAN. The upper two sentences are sampled fromthe real-world data and the lower two sentences are sampled from the generated data

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