A dynamic minimal model of the listener for feedback-based dialogue coordination

Although the notion of grounding in dialogue is widely acknowledged, the exact nature of the representations of common ground and its specific role in language processing are topics of ongoing debate. Proposals range from rich, explicit representations of common ground in the minds of speakers (Clark,1996) to implicit representations, or even none at all (Pickering and Garrod, 2004). We argue that a minimal model of mentalising that tracks the interlocutor's state in terms of general states of perception, understanding, acceptance and agreement, and is continuously updated based on communicative listener feedback, is a viable and practical concept for the purpose of building conversational agents. We present such a model based on a dynamic Bayesian network that takes listener feedback and dialogue context into account, and whose temporal dynamics are modelled with respect to discourse structure. The potential benefit of this approach is discussed with two applications: generation of feedback elicitation cues, and anticipatory adaptation.