Dialogue Generation With GAN

This paper presents a Generative Adversarial Network (GAN) to model multi-turn dialogue generation, which trains a latent hierarchical recurrent encoder-decoder simultaneously with a discriminative classifier that make the prior approximate to the posterior. Experiments show that our model achieves better results.

Authors

• Hui Su
• Xiaoyu Shen
• Pengwei Hu Xinjiang Technical Institute of Physics and Chemistry, Urumqi, China University of Chinese Academy of Sciences, Beijing, China
• Wenjie Li Computation Department, The Hong Kong Polytechnic University, Hong Kong
• Yun Chen

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