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。

MindDial：基于心理理论建模的信念动态追踪技术，用于实境神经对话生成

MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for  Situated Neural Dialogue Generation

From its inception, AI has had a rather ambivalent relationship to
humans---swinging between their augmentation and replacement. Now, as AI
technologies enter our everyday lives at an ever increasing pace, there is a
greater need for AI systems to work synergistically with humans. To do this
effectively, AI systems must pay more attention to aspects of intelligence that
helped humans work with each other---including social intelligence. I will
discuss the research challenges in designing such human-aware AI systems,
including modeling the mental states of humans in the loop, recognizing their
desires and intentions, providing proactive support, exhibiting explicable
behavior, giving cogent explanations on demand, and engendering trust. I will
survey the progress made so far on these challenges, and highlight some
promising directions. I will also touch on the additional ethical quandaries
that such systems pose. I will end by arguing that the quest for human-aware AI
systems broadens the scope of AI enterprise, necessitates and facilitates true
inter-disciplinary collaborations, and can go a long way towards increasing
public acceptance of AI technologies.

讨论了设计人类感知人工智能系统面临的挑战，包括建模人类的心理状态、识别其愿望和意图、提供主动支持、展示可解释性行为、并引发信任等，并强调这种系统引出的伦理困境和推广真正跨学科合作的必要性。