  For the task of recognizing dialogue acts, we are applying the
Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a
sparse data problem, we extract values of well-motivated features of
utterances, such as speaker direction, punctuation marks, and a new feature,
called dialogue act cues, which we find to be more effective than cue phrases
and word n-grams in practice. We present strategies for constructing a set of
dialogue act cues automatically by minimizing the entropy of the distribution
of dialogue acts in a training corpus, filtering out irrelevant dialogue act
cues, and clustering semantically-related words. In addition, to address
limitations of TBL, we introduce a Monte Carlo strategy for training
efficiently and a committee method for computing confidence measures. These
ideas are combined in our working implementation, which labels held-out data as
accurately as any other reported system for the dialogue act tagging task.
