Natural Language Dialogue group

Virtual human characters equipped with natural language dialogue capability have proved useful in many fields like simulation training and interactive games. Generally behind such dialogue managers lies a complex knowledge-rich rule-based system. Building such system involves meticulous annotation of data and hand autoring of rules. In this paper we build a statistical dialogue model from roleplay and wizard of oz dialog corpus with virtually no annotation. We compare these methods with the traditional approaches. We have evaluated these systems for perceived appropriateness of response and the results are presented here.