Domain-independent User Simulation With Transformers For Task-oriented Dialogue Systems

Lin Hsien-chin, Lubis Nurul, Hu Songbo, Van Niekerk Carel, Geishauser Christian, Heck Michael, Feng Shutong, Gašić Milica. Arxiv 2021

[Paper]    
Agentic Applications Model Architecture Pretraining Methods Reinforcement Learning Training Techniques Transformer

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.

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