  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. State-of-the-art data-driven user simulators, on the other hand,
are still domain-dependent. This means that adaptation to each new domain
requires redesigning and retraining. In this work, we propose a
domain-independent transformer-based user simulator (TUS). The structure of our
TUS is not tied to a specific domain, enabling domain generalisation and
learning of cross-domain user behaviour from data. We compare TUS with the
state of the art using automatic as well as human evaluations. TUS can compete
with rule-based user simulators on pre-defined domains and is able to
generalise to unseen domains in a zero-shot fashion.
