Paper: Dialogue Act Tagging with Transformation-Based Learning

ACL ID P98-2188
Title Dialogue Act Tagging with Transformation-Based Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998

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- i...