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We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. . | Identifying Agreement and Disagreement in Conversational Speech Use of Bayesian Networks to Model Pragmatic Dependencies Michel Galley Kathleen McKeown Julia Hirschberg and Elizabeth Shribergl Columbia University SRI International Computer Science Department Speech Technology and Research Laboratory 1214 Amsterdam Avenue 333 Ravenswood Avenue New York NY 10027 USA Menlo Park CA 94025 USA galley kathy julia @cs.columbia.edu ees@speech.sri.com Abstract We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical durational and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. Our approach achieves 86.9 accuracy a 4.9 increase over previous work. 1 Introduction One of the main features of meetings is the occurrence of agreement and disagreement among participants. Often meetings include long stretches of controversial discussion before some consensus decision is reached. Our ultimate goal is automated summarization of multi-participant meetings and we hypothesize that the ability to automatically identify agreement and disagreement between participants will help us in the summarization task. For example a summary might resemble minutes of meetings with major decisions reached consensus along with highlighted points of the pros and cons for each decision. In this paper we present a method to automatically classify utterances as agreement disagreement or neither. Previous work in automatic identification of agreement disagreement Hillard et al. 2003 demonstrates that this is a feasible task when various textual durational and acoustic features are available. We build on their approach and show that