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Neural Emotional Response Generation via Adversarial Transfer Learning

Emotional response generation is a key step to build an empathetic chatbot. However, previous emotional chatting models mainly focus on single-turn conversation, and multi-turn context emotional response generation has not been explored. In this paper, we propose an adversarial transfer emotional chatting (ATEC) model for multi-turn conversation which is based on conditional variational autoencoders (CVAE). ATEC has two alternate training phases: supervised training and transfer training. In the supervised training stage, we train the CVAE model, a content discriminator and an emotional classifier based on ground truth corpus. And in the transfer training stage, we change the target emotion and use the content discriminator to force the model to transfer the multi-turn context information, while the emotional classifier regularizes the emotions expressed in the generated responses. Experiments show that the proposed approach achieves state of the art performance with diverse responses and accurate emotional expression.