Statistical and Empirical approaches for spoken dialog systems
Workshop proposal for AAAI-06 (Boston)
Organizers: Jason D. Williams, Steve Young, Pascal Poupart, Stephanie Seneff
1) Workshop topic
A description of the workshop topic. Identify the specific issues on which the
workshop will focus.
Spoken dialog systems are machines which interact with people using spoken
language. This workshop seeks to draw new work on statistical and empirical
approaches for spoken dialog systems. We welcome both theoretical and
applied work, addressing issues such as:
-
Representations and data structures for dialog models suitable for
machine learning
Methods for automatic generation and improvement of dialog managers
incorporating machine learning
Ontology representations and integration methods suitable for machine
learning
Techniques to accurately simulate human-computer dialog
Creation, use, and evaluation of user models
Methods for automatic evaluation of dialogue systems
Investigations into appropriate optimization criteria for spoken dialog
systems
Applications and real-world examples of spoken dialog systems
incorporating statistical or empirical techniques
Use of statistical or empirical techniques within multi-modal dialog
systems
Application of statistical or empirical techniques to multi-lingual spoken
dialog systems
The use and application of techniques and methods from related areas,
such as cognitive science, operations research, emergence models, etc.
2) Motivation
A brief discussion of why the topic is of particular interest at this time.
Although the low-level speech recognition component of spoken dialog systems
has long been framed as a statistical pattern classifier trained on data, most
approaches to the higher-level dialog management components have been
handcrafted.
Recently a number of researchers have begun exploring how dialog
management can be approached as a machine learning problem. This interest
has been driven by several factors:
-
-
Growing availability of dialog data corpora
Emergence of new optimization techniques and computing power able to
scale to dialog management problems – for example, in reinforcement
learning
Realization that the design and testing of spoken dialog systems is timeconsuming and expensive
Failure of hand-crafted approaches to dialog management to demonstrate
robust behavior in the face of inaccurate speech recognition, and move
reliably beyond simple types of systems.
3) Format
A brief description of the proposed workshop format, regarding the mix of events
such as paper presentations, invited talks, panels, and general discussion.
We envisage approximately 3 paper presentation sessions (each with
approximately 4 papers) mixed with approximately 2 invited speakers.
For the invited speakers, we envisage distinguished members of the
dialog/speech community and the machine learning community. We have
identified several candidates for speakers but have not approached speakers yet.
Our aims for invited speakers are to: provide views on issues such as how dialog
management/dialog modeling can be represented as a machine learning
problem, explain methods for machine learning of interest to the dialog
management community, suggest how to scale machine learning to problems in
this domain, and propose interesting research questions.
For the paper sessions, we would like to foster interaction & discussion. After
each paper is presented, time will be left for questions and discussions. At the
end of each session, additional time will be reserved for general discussion about
that session as a whole.
4) Length
An indication as to whether the workshop should be considered for a half-day,
one or two-day meeting.
We envisage a one-day meeting.
5) Organizing committee
The names and full contact information (email and postal addresses, fax and
telephone numbers) of the organizing committee-three or four people
knowledgeable in the field-and short descriptions of their relevant expertise.
Strong proposals include organizers who bring differing perspectives to the
workshop topic and who are actively connected to the communities of potential
participants.
Jason D. Williams
University of Cambridge
53A Marlow Road
London
SE20 7YG
United Kingdom
+44 7786 683 013
jdw30@cam.ac.uk
Jason Williams has been working full-time on spoken dialog systems for the past
8 years, dividing his time evenly between research and commercial deployments.
In industry, he has built telephone-based spoken dialog systems for a host of
companies such as Sony, BMW, Lowe’s, Travelocity, and the Home Shopping
Network. In research, he has focused on applying Partially Observable Markov
Decision Processes (POMDPs) to dialog management problems. In this pursuit,
he has explored data collection methods, dialog model representations, and
optimization techniques for POMDPs.
Steve Young
University of Cambridge
Engineering Department
Trumpington Street
Cambridge
CB2 1PZ
+44 1223 332 654
sjy@eng.cam.ac.uk
Steve Young is Head of the Information Engineering Division at Cambridge
University. Previously he was Chief Scientist at Entropic Inc and an Architect in
the Speech Products group at Microsoft. He has experience of using statistical
methods in all aspects of speech and language processing including recognition,
understanding and dialogue management. His most recent work conducted as
part of the European EC Talk Project has focused on applying Partially
Observable MDPs to practical dialogue information systems.
Pascal Poupart
School of Computer Science
University of Waterloo
200 University Avenue West
Waterloo, Ontario
Canada N2L 3G1
+1 519 888 4567 x 6239
ppoupart@cs.uwaterloo.ca
Pascal Poupart is an assistant professor in the school of Computer Science
at the University of Waterloo in Canada. His research focuses on the
development of decision-theoretic planning and statistical machine
learning techniques, which he has applied to a range of applications,
including spoken dialog systems, assistive technologies for dementia
patients and ontology learning. In particular, some of his recent work
include the development of robust dialogue management algorithms based on
partially observable Markov decision processes.
Stephanie Seneff
Spoken Language Systems Group
MIT Computer Science and Artificial Intelligence Laboratory
MIT Stata Center
32 Vassar Street
Cambridge, MA 02139
USA
+1 617 253 0451
seneff@csail.mit.edu
Stephanie Seneff is a Principal Research Scientist in the Spoken Language
Systems group at the Computer Science and Artificial Intelligence Laboratory at
MIT. She has been conducting research on all aspects of spoken dialogue
system development for the past 15 years, and has played a significant role in
the development of mixed-initiative telephone-access dialogue systems in many
different domains (weather, flights, restaurants, etc.) Her recent interests include
generic spoken language understanding, generic dialogue modeling, portability
and robustness in dialogue systems, user simulation, and multimodal and
multilingual dialogue systems.
6) Potential attendees
A list of potential attendees.
Note: the attendees listed below have not been contacted – this is an illustrative
list of people who are either active in this area, or who have attended similar
workshops in the recent past:
Ingrid Zukerman, Monash University, Australia
Jan Alexandersson, DFKI GmbH, Germany
Arne Jönsson, Linköping University, Sweden
Geniveve Gorrell, Linköping University, Sweden
Dan Bohus, Carnegie Mellon University, USA
Tim Paek, Microsoft Research, USA
Alex Rudnicky, Carnegie Mellon University, USA
Jim Glass, MIT, USA
Victor Zue, MIT, USA
Grace Chung, MIT, USA
Jost Schatzmann, University of Cambridge, USA
Alex Gruenstein, MIT, USA
Ed Filisko, MIT, USA
Matthias Denecke, NTT Computer Science Laboratories, Japan
Ian Lane, ATR Spoken Language Communication Research Labpratories, Japan
Mihai Rotaru, Univeristy of Pittsburg, USA
Nils Dahlbäck, Linköping University, Sweden
Diane Litman, University of Pittsburg, USA
Marilyn Walker, University of Sheffield, UK
Joe Polifroni, University of Sheffield, UK
Nate Blaylock, Saarland University, Germany
Antoine Raux, Carnegie Mellon University, USA
Verena Rieser, Saarland University, Germany
Jost Schatzmann, Cambridge University, UK
Gabriel Skantze, KTH - Royal Institute of Technology, Sweden
Matt Stuttle, University of Cambridge, UK
Stefanie Tomko, Carnegie Mellon University, USA
Oliver Lemon, University of Edinburgh, UK
Jamie Henderson, University of Edinburgh, UK
Roi Georgila, University of Edinburgh, UK
Ryuichiro Higashinaka, University of Sheffield, UK
Stephen Choularton, Macquarie University, Australia
Stephen Cox, University of East Anglia, UK
Gokham Tur, AT&T Research, USA
Dilek Hakkani-Tur, AT&T Research, USA
Guiseppe di Fabbrizio, AT&T Research, USA
Dan Jurafsky, Stanford University, USA
Manny Rayner, NASA, USA
Elizabeth Shriberg, SRI, USA
Johan Boye, Telia Research, Sweden
Sandra Carberry, University of Delaware, USA
Peter Heeman, Oregon Graduate Institute, USA
Eric Horvitz, Microsoft Research, USA
Kazunori Komatani, Kyoto University, Japan
Staffan Larsson, Göteborgs Universitet, Sweden
Michael McTear, University of Ulster, UK
Norbert Reithinger, DFKI, Germany
Candy Sidner, MERL, USA
David Traum, USC Institute for Creative Technology, USA
Joelle Pineau, McGill University, Canada
Nick Roy, MIT, USA
Satinder Singh, U of Michigan, USA