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Báo cáo khoa học: "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. . | Dialogue Act Tagging with Transformation-Based Learning Ken Samuel and Sandra Carberry and K. Vijay-Shanker Department of Computer and Information Sciences University of Delaware Newark Delaware 19716 USA samuel Carberry vijay @cis. udel.edu http www.eecis.udel.edu samuel Carberry vijay Abstract 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. Introduction Although machine learning approaches have achieved success in many areas of Natural Language Processing researchers have only recently begun to investigate applying machine learning methods to discourse-level problems Re-ithinger and Klesen 1997 Di Eugenio et al. 1997 Wiebe et al. 1997 Andernach 1996 Lit-man 1994 . An important task in discourse understanding is to interpret an utterance s dialogue act which is a concise abstraction of a speaker s intention such as SUGGEST and ACCEPT. Recognizing dialogue acts is critical for discourse-level understanding and can also be useful for other applications such as resolving ambiguity in speech recognition. However com puting dialogue acts is a challenging task because often a