  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.
