The purpose of this workshop is to bring together researchers investigating the application of learning and adaptation to dialogue systems, both speech and text based.

In this workshop we encourage papers on either theoretical or applied research in adaptation for dialogue, that includes learning procedures as well as decision making methods aimed at dynamically reconfiguring dialogue behavior based on the context. We would also like to explore techniques that allow a dialogue system to learn with experience or from data sets gathered from empirical studies. We welcome submissions from researchers supplementing the traditional development of dialogue systems with techniques from machine learning, statistical NLP, and decision theory.

We solicit papers from a number of research areas, including:

• Use of machine learning techniques at all levels of dialogue, from speech recognition to generation; from dialogue strategy to user modeling
• Adapting to the user as a dialogue progresses
• Dialogue as decision making under uncertainty
• User and user group modeling
• Use of corpora in developing components of dialogue systems, including issues in annotation
• Evaluation of adaptive dialogue systems
• Comparison of different techniques in applying adaptive techniques to dialogue

A web site that will provide additional information on the workshop as it becomes available is located at:

For more information:
Please direct questions to Eric Horvitz (horvitz@microsoft.com ), Tim Paek (timpaek@microsoft.com ), or Cindi Thompson (cindi@cs.utah.edu ).