Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems

Understand

Dialogue policy optimisation via reinforcement learning requires a large number of training interactions, which makes learning with real users time consuming and expensive.

• Many set-ups therefore rely on a user simulator instead of humans.
• These user simulators have their own problems.
• While hand-coded, rule-based user simulators have been shown to be sufficient in small, simple domains, for complex domains the number of rules quickly becomes intractable.