Dialogue Act Tagging with Transformation-Based Learning

Details Dialogue Act Tagging with Transformation-Based Learning Dialogue Act Tagging with Transformation-Based Learning

1998-06-08

arxiv.org/abs/cmp-lg/9806006v1

Proceedings of the 17th International Conference on Computational Linguistics (COLING-ACL '98)

Computer Science

Computation and Language

7 pages, no Postscript figures, uses colacl.sty and acl.bst

Scientific paper

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.

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