  This paper presents results from the first attempt to apply
Transformation-Based Learning to a discourse-level Natural Language Processing
task. To address two limitations of the standard algorithm, we developed a
Monte Carlo version of Transformation-Based Learning to make the method
tractable for a wider range of problems without degradation in accuracy, and we
devised a committee method for assigning confidence measures to tags produced
by Transformation-Based Learning. The paper describes these advances, presents
experimental evidence that Transformation-Based Learning is as effective as
alternative approaches (such as Decision Trees and N-Grams) for a discourse
task called Dialogue Act Tagging, and argues that Transformation-Based Learning
has desirable features that make it particularly appealing for the Dialogue Act
Tagging task.
