Pattern and content controlled response generation

Abstract Controllable response generation is an attractive and valuable task to the success of conversational systems. However, controlling both pattern and content of the response has not been well studied in existing models since they are mainly based on matching mechanisms. To tackle the problem, we first design a pattern model to automatically learn and extract speech patterns from words. The pattern is then integrated into the encoder–decoder model to control the response pattern. Second, a sentence sampling algorithm is built to directly insert or delete words in the generated response, so that the content is controlled. In this two-stage framework, the response could be explicitly controlled by the pattern and content, without any human annotation of the post-response dataset. Experiments show the proposed framework achieves better performance in response controllability than the state-of-the-art.