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.
REF proposed the use of Bayesian networks to model pragmatic dependencies of previous agreement or disagreement on the current utterance.
Identifying Agreement And Disagreement In Conversational Speech: Use Of Bayesian Networks To Model Pragmatic Dependencies
Annual Meeting Of The Association For Computational Linguistics
Computer Science