Thursday, June 1st at 11am.

Speaker: Milica Gasic, lecturer, University of Cambridge, UK.


In the last decade we have witnessed machine learning trigger a revolution in dialogue research. Using a variety of reinforcement and supervised learning methods and innovative architectures, we can now build fully data-driven dialogue systems. These techniques are part of a user-in-the-loop framework, where systems can be deployed quickly, handle speech recognition errors gracefully and learn continuously from interaction with real users.

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 uncertainty, which is particularly useful for reinforcement learning. This talk explores the additional steps that are necessary to extend these methods to support adaptation to different dialogue domains and more data efficient learning, an important step for scaling up and building evolving systems.

The final part of the talk will focus on the evolution of the next generation of spoken dialogue systems. These systems will need to operate on large and dynamic domains and, more importantly, be capable of conducting rich and natural interaction. We will present a research roadmap towards this goal. A typical application where such a level of complexity is needed is a mental health application that we will discuss to illustrate the need for such research