Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies

Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies Michel Galley � , Kathleen McKeown � , Julia Hirschberg � , � Columbia University Computer Science Department 1214 Amsterdam Avenue New York, NY 10027, USA � galley,kathy,julia � @cs.columbia.edu and Elizabeth Shriberg � � SRI International Speech Technology and Research Laboratory 333 Ravenswood Avenue Menlo Park, CA 94025, USA ees@speech.sri.com Abstract We describe a statistical approach for modeling agreements and disagreements in conversational in- teraction. 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 dis- agreement using these adjacency pairs and features that represent various pragmatic influences of pre- vious agreement or disagreement on the current ut- terance. Our approach achieves 86.9% accuracy, a 4.9% increase over previous work. 1 Introduction One of the main features of meetings is the occur- rence of agreement and disagreement among par- ticipants. Often meetings include long stretches of controversial discussion before some consensus decision is reached. Our ultimate goal is auto- mated summarization of multi-participant meetings and we hypothesize that the ability to automatically identify agreement and disagreement between par- ticipants 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 var- ious textual, durational, and acoustic features are available. We build on their approach and show that we can get an improvement in accuracy when contextual information is taken into account. Our approach first identifies adjacency pairs using maxi- mum entropy ranking based on a set of lexical, dura- tional and structural features that look both forward and backward in the discourse. This allows us to ac- quire, and subsequently process, knowledge about who speaks to whom. We hypothesize that prag- matic features that center around previous agree- ment between speakers in the dialog will influence the determination of agreement/disagreement. For example, if a speaker disagrees with another per- son once in the conversation, is he more likely to disagree with him again? We model context using Bayesian networks that allows capturing of these pragmatic dependencies. Our accuracy for classify- ing agreements and disagreements is 86.9%, which is a 4.9% improvement over (Hillard et al., 2003). In the following sections, we begin by describ- ing the annotated corpus that we used for our ex- periments. We then turn to our work on identify- ing adjacency pairs. In the section on identification of agreement/disagreement, we describe the contex- tual features that we model and the implementation of the classifier. We close with a discussion of future work. 2 Corpus The ICSI Meeting …

Content truncated to 500 words