Conversational Response Re-ranking Based on Entrainment Prediction

Abstract

Entrainment is a phenomenon observed in human-human conversation, which is a synchronization of speaking style according to dialogue progress. In this study, we propose a method to build an entrainable chitchat system by predicting the ideal entrainment score for the given dialogue history. The proposed method reranks existing neural conversation model outputs based on the predicted entrainment score. We conducted automatic and human-subjective evaluations to investigate the effect of the proposed method by comparing it with the system response without using the reranking system. The experimental results showed that our proposed method achieves ideal entrainment while maintaining the naturalness of the generated responses compared to the baseline method.