  Building a socially intelligent agent involves many challenges. One of which
is to track the agent's mental state transition and teach the agent to make
decisions guided by its value like a human. Towards this end, we propose to
incorporate mental state simulation and value modeling into dialogue agents.
First, we build a hybrid mental state parser that extracts information from
both the dialogue and event observations and maintains a graphical
representation of the agent's mind; Meanwhile, the transformer-based value
model learns human preferences from the human value dataset, ValueNet.
Empirical results show that the proposed model attains state-of-the-art
performance on the dialogue/action/emotion prediction task in the fantasy
text-adventure game dataset, LIGHT. We also show example cases to demonstrate:
(i) how the proposed mental state parser can assist the agent's decision by
grounding on the context like locations and objects, and (ii) how the value
model can help the agent make decisions based on its personal priorities.
