Humans talk in free-form while negotiating the expressed meanings or common
ground. Despite the impressive conversational abilities of the large generative
language models, they do not consider the individual differences in contextual
understanding in a shared situated environment. In this work, we propose
MindDial, a novel conversational framework that can generate situated free-form
responses to negotiate common ground. We design an explicit mind module that
can track three-level beliefs -- the speaker's belief, the speaker's prediction
of the listener's belief, and the common belief based on the gap between the
first two. Then the speaking act classification head will decide to continue to
talk, end this turn, or take task-related action. We augment a common ground
alignment dataset MutualFriend with belief dynamics annotation, of which the
goal is to find a single mutual friend based on the free chat between two
agents. Experiments show that our model with mental state modeling can resemble
human responses when aligning common ground meanwhile mimic the natural human
conversation flow. The ablation study further validates the third-level common
belief can aggregate information of the first and second-order beliefs and
align common ground more efficiently.

MindDial 是一种新的对话框架，可以生成特定语境下的自由形式响应来协商共同点。