Abstract

For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circum-vent 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 strate-gies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training cor-pus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we in-troduce a Monte Carlo strategy for training ef-ficiently and a committee method for comput-ing confidence measures. These ideas are com-bined in our working implementation, which la-bels held-out data as accurately as any other reported system for the dialogue act tagging task.

Citations