Paper: Identifying Agreement And Disagreement In Conversational Speech: Use Of Bayesian Networks To Model Pragmatic Dependencies

ACL ID P04-1085
Title Identifying Agreement And Disagreement In Conversational Speech: Use Of Bayesian Networks To Model Pragmatic Dependencies
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

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.