A SVM Cascade for Agreement/Disagreement Classification

This article describes a method for classifying dialogue utterances and detecting the interlocutor’s agreement or disagreement. This labelling can help improve dialogue management by providing additional information on the utterance’s content without deep parsing. The proposed technique improves upon state of the art approaches by using a Support Vector Machine cascade. A combination of three binary support vector machines in a cascade is employed to filter out utterances that are easy to classify, thus reducing the noise in the learning of labels for more ambiguous utterances. The approach achieves higher accuracy (by 2.47%) than the state of the art while using a simpler approach which relies only on shallow local features of the utterances