Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning

Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not wellcontrolled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a rolebased contextual model to consider different speaker roles independently based on the various speaking patterns in the multiturn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns rolespecific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks1.