  The performance of a task-completion dialogue agent usually affects the user
experience: when the conversation system yields an unreasonable response, users
may feel dissatisfied. Besides, early termination often occurs in disappointing
conversations. However, existing off-the-shelf user simulators generally assume
an ideal and cooperative user, which is somewhat different from a real user,
and inevitably lead to a sub-optimal dialogue policy. In this paper, we propose
an emotion-aware user simulation framework for task-oriented dialogue, which is
based on the OCC emotion model to update user emotions and drive user actions,
to generate simulated behaviors that more similar to real users. We present a
linear implementation (The source code will be released soon.) that is easy to
understand and extend, and evaluate it on two domain-specific datasets. The
experimental results show that the emotional simulation results of our proposed
framework conform to common sense and have good versatility for different
domains. Meanwhile, our framework provides us with another perspective to
understand the improvement process of the dialogue policy model based on
reinforcement learning.
