Adaptation in Dialogue Systems

Links:

• NAACL 2001
• Instructions for Authors
• Call For Papers (Expired)
• Important Dates
• Workshop Schedule

Overview

Methods for learning and adaptation show promise for enhancing the robustness, flexibility, and overall accuracy of dialogue systems. While researchers in many parts of computational linguistics who use these methods have begun to form communities, the burgeoning set of activities within dialogue has remained relatively disparate. We are interested in adaptation 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. Researchers looking at methods to automatically improve different modules of dialogue systems, or the system as a whole, have not had many opportunities to come together to share their work. We thus welcome submissions from researchers supplementing the traditional development of dialogue systems with techniques from machine learning, statistical NLP, and decision theory.

Call For Papers

Important Dates (2001):