Dialogue Act Tagging with Transformation-Based Learning
<|reference_start|>Dialogue Act Tagging with Transformation-Based Learning: 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.<|reference_end|>
@article{samuel1998dialogue,
    title={Dialogue Act Tagging with Transformation-Based Learning},
    author={Ken Samuel, Sandra Carberry, and K. Vijay-Shanker (Department of
  Computer and Information Sciences, University of Delaware)},
    journal={Proceedings of the 17th International Conference on Computational
  Linguistics (COLING-ACL '98)},
    year={1998},
    archivePrefix={arXiv},
    eprint={cmp-lg/9806006},
    primaryClass={cmp-lg cs.CL}
}