Hierarchical speech-act classification for discourse analysis

The analysis of a speech act is important for dialogue understanding systems because the speech act of an utterance is closely associated with the user’s intention in the utterance. This paper proposes a speech act classification model that effectively uses a two-layer hierarchical structure generated from the adjacency pair information of speech acts. The proposed model has two advantages when adding hierarchical information to speech act classification; the improved accuracy of the speech act classification and the reduced running time in the testing phase. As a result, it achieves higher performance than other models that do not use the hierarchical structure and has faster running time because Support Vector Machine classifiers can efficiently be arranged on the two-layer hierarchical structure.