Statistical and Empirical Approaches to Spoken Dialog Systems
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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.
Statistical and Empirical Approaches to Spoken Dialog Systems
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
time- consuming 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 [email protected]
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,
Lowes, 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 [email protected] 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 [email protected] 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 [email protected] 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
Jnsson, Linkping University, Sweden Geniveve Gorrell, Linkping
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 Dahlbck, Linkping 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,
Gteborgs 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