Keynote Speakers

Professor Lillian Lee, Cornell University, USA

Language Adaptation

As we all know, more and more of life is now manifested online, and many of the digital traces that are left by human activity are increasingly recorded in natural-language format. This availability offers us the opportunity to glean user-modeling information from individual users' linguistic behaviors. This talk will discuss the particular phenomenon of individual language adaptation, both in the short term and in the longer term. We'll look at connections between how people adapt their language to particular conversational partners or groups, on the one hand, and on the other hand, those people's relative power relationships, quality of relationship with the conversational partner, and propensity to remain a part of the group.

Lillian Lee is a Professor of Computer Science at Cornell University. She is the recipient of the inaugural Best Paper Award at HLT-NAACL 2004 (joint with Regina Barzilay), a citation in "Top Picks: Technology Research Advances of 2004" by Technology Research News (also joint with Regina Barzilay), and an Alfred P. Sloan Research Fellowship; and in 2013, she was named a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Her group's work has received several mentions in the popular press, including The New York Times, NPR's All Things Considered, and NBC's The Today Show.

Professor Steve Young, University of Cambridge, UK

Statistical Approaches to Open-domain Spoken Dialogue Systems

In contrast to traditional rule-based approaches to building spoken dialogue systems, recent research has shown that it is possible to implement all of the required functionality using statistical models trained using a combination of supervised learning and reinforcement learning. This approach to spoken dialogue is based on the mathematics of partially observable Markov decision processes (POMDPs) in which user inputs are treated as observations of some underlying belief state, and system responses are determined by a policy which maps belief states into actions.

Virtually all current spoken dialogue systems are designed to operate in either a specific carefully defined domain such as restaurant information and appointment booking, or they have very limited conversational ability such as in Siri and Google Now. However, if voice is to become a significant input modality for accessing web-based information and services, then techniques will be needed to enable conversational spoken dialogue systems to operate within open domains.

This talk will discuss methods by which current statistical approaches to spoken dialogue can be extended to cover much wider domains. It will be argued that unlike many other areas of machine learning, spoken dialogue systems always have a user on-hand to provide supervision. Hence spoken dialogue systems provide a unique opportunity to automatically adapt on large quantities of speech data without the need for costly annotation.

Steve Young is Professor of Information Engineering and Senior Pro-Vice Chancellor at Cambridge University. His main research interests lie in the area of spoken language systems including speech recognition, speech synthesis and dialogue management. He is the inventor and original author of the HTK Toolkit for building hidden Markov model-based recognition systems, and he co-developed the HTK large vocabulary speech recognition system. More recently he has worked on statistical dialogue systems and pioneered the use of Partially Observable Markov Decision Processes for modelling them.

He is a Fellow of the Royal Academy of Engineering, the International Speech Communication Association, the Institution of Engineering and Technology, and the Institute of Electrical and Electronics Engineers. In 2004, he was a recipient of an IEEE Signal Processing Society Technical Achievement Award; in 2010, he received the ISCA Medal for Scientific Achievement; and in 2013, he received the European Signal Processing Society Individual Technical Achievement Award.

Last update: March 10, 2014 - Credits