Towards Emotion-Aware User Simulator for Task-Oriented Dialogue

TL;DR

This paper introduces an emotion-aware user simulator for task-oriented dialogue systems, aiming to produce more realistic user behaviors and improve dialogue policy learning.

Contribution

It proposes a novel emotion-driven simulation framework based on the OCC model, enhancing realism and versatility over traditional simulators.

Findings

Simulated behaviors align with common sense and real user patterns.

Framework demonstrates good adaptability across different domains.

Provides new insights into reinforcement learning-based dialogue policy improvement.

Abstract

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.