Sunday 2nd December
Miami Beach Resort & Spa
Miami, Floria, USA
Talk Title: Statistical Dialogue Management for Conversational Spoken Interfaces: Why, How and Scaling-up.
Recent commercial systems, such as Siri, have significantly increased public awareness of spoken dialogue systems (SDS). However such commercial system tend to sidestep the problem of maintaining a conversation, instead adopting a one-shot question-answer, command-control or voice-search approach.
Even in relatively simple tasks, such as flight booking or providing tourist information, the ability to maintain a conversation has significant advantages. Ambiguities (either due to noisy recognition or confusable semantics) can be resolved through judicious selection of speech acts by the SDS Dialogue Manager (SDS DM).
Statistical models provide a particularly attractive approach to building conversational systems. The different types of uncertainty that arise, e.g. uncertainty associated with recognition or identifying the context, goals or conversation state, can be directly modelled. Information arising from different turns in the dialogue can be integrated and the conversation's state updated in a principled manner. There is also no need to design cumbersome sub-dialogues to recover from dialogue mistakes. A statistical model naturally integrates any negation, rejection or correction into it's update of the conversation state.
Recent research has demonstrated the expected improvements in robustness when using statistical models for dialogue management. These models being coupled with techniques for automatic policy optimisation. However, scaling up to tackle real world problem is still an area of active research.
This tutorial will provide an introduction to statistical dialogue management and outline the advantages that statistical models provide. It will look at current statistical models found in SDS DM research, e.g. Bayesian networks and POMDPs. Illustraite how such systems are created and policies trained using both POMDP planning and the latest sample efficient Reinforcement Learning techniquea. It will finishing with a discussion of current work on scaling up such models to handle sufficent contextual information that real world problems can be tackled while retaining robustness, (e.g. Mixture-Model POMDPs, Summary Spaces, Automatic Belief Compression and large scale POMDP solvers).
Workshop: Understanding Image Processing3rd – 4th November 2011
Wellcome Trust Clinical Research Facility.
Western General Hospital. Edinburgh.
Talk title: Machine Learning: an introduction.
Audience: Medical practitioners and cognitive-psychologists.
Workshop designed for those who work in clinical and cognitive-related areas who do not have a background in computer sciences or image processing.