Cas GANs: An Approach of Dialogue Policy Learning based on GAN and RL Techniques
Dialogue management systems are commonly applied in daily life, such as online shopping, hotel booking, and driving booking. Efficient dialogue management policy helps systems to respond to the user in an effective way. Policy learning is a complex task to build a dialogue system. There are different approaches have been proposed in the last decade to build a goal oriented dialogue agent to train the systems with an efficient policy. The Generative adversarial network (GAN) is used in the dialogue generation, in previous works to build dialogue agents by selecting the optimal policy learning. The efficient dialogue policy learning aims to improve the quality of fluency and diversity for generated dialogues. Reinforcement learning (RL) algorithms are used to optimize the policies because the sequence is discrete. In this study, we have proposed a new technique called Cascade Generative Adversarial Network (Cas GAN) that is combination of the GAN and RL for dialog generation. The Cas GAN can model the relations between the dialogues (sentences) by using Graph Convolutional Networks (GCN) The graph nodes are consisting of different high level and low level nodes representing the vertices and edges of the graph. Then, we use the maximum log likelihood (MLL) approach to train the parameters and choose the best nodes. The experimental results compared with the HRL, RL agents and we got state of the art results.