Existing user simulators (USs) for task-oriented dialogue systems only model user behaviour on semantic and natural language levels without considering the user persona and emotions. Optimising dialogue systems with generic user policies, which cannot model diverse user behaviour driven by different emotional states, may result in a high drop-off rate when deployed in the real world. Thus, we present EmoUS, a user simulator that learns to simulate user emotions alongside user behaviour. EmoUS generates user emotions, semantic actions, and natural language responses based on the user goal, the dialogue history, and the user persona. By analysing what kind of system behaviour elicits what kind of user emotions, we show that EmoUS can be used as a probe to evaluate a variety of dialogue systems and in particular their effect on the user's emotional state. Developing such methods is important in the age of large language model chat-bots and rising ethical concerns.

翻译：现有面向任务导向对话系统的用户模拟器仅在语义和自然语言层面建模用户行为，未考虑用户角色与情绪。使用无法建模不同情绪状态驱动下多样化用户行为的通用用户策略优化对话系统，在实际部署时可能导致高流失率。为此，我们提出EmoUS——一种学习模拟用户情绪及其行为的用户模拟器。该模拟器基于用户目标、对话历史和用户角色生成用户情绪、语义动作及自然语言回复。通过分析何种系统行为会引发何种用户情绪，我们证明EmoUS可作为探针评估各类对话系统，特别是其对用户情绪状态的影响。在大语言模型聊天机器人时代及伦理问题日益凸显的背景下，此类方法的开发具有重要意义。