  Spoken dialogue systems allow humans to interact with machines using natural
speech. As such, they have many benefits. By using speech as the primary
communication medium, a computer interface can facilitate swift, human-like
acquisition of information. In recent years, speech interfaces have become ever
more popular, as is evident from the rise of personal assistants such as Siri,
Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning
methods have been applied to dialogue modelling and the results achieved for
limited-domain applications are comparable to or outperform traditional
approaches. Methods based on Gaussian processes are particularly effective as
they enable good models to be estimated from limited training data.
Furthermore, they provide an explicit estimate of the uncertainty which is
particularly useful for reinforcement learning. This article explores the
additional steps that are necessary to extend these methods to model multiple
dialogue domains. We show that Gaussian process reinforcement learning is an
elegant framework that naturally supports a range of methods, including prior
knowledge, Bayesian committee machines and multi-agent learning, for
facilitating extensible and adaptable dialogue systems.
