Dialog Act Classification Using Features Intrinsic to Dialog Acts in an Open-Domain Conversation

Abstract

The classification of dialog acts of user’s utterance is one of the important fundamental techniques in open-domain conversational systems. Most previous studies on the classification of dialog acts were based on supervised machine learning; however, the characteristics of individual dialog acts were not considered. Some features for machine learning may increase the accuracy of classification for a particular dialog act, whereas decrease the accuracy for other dialog acts. In this study, an appropriate feature set is defined for each dialog act to improve the performance of the classification of the dialog acts. First, 28 features are proposed as an initial set. Second, for each dialog act, an optimal set of the features is identified by removing ineffective features from the initial set. Third, binary classifiers that judge whether a dialog act is suitable for a given utterance are trained using the optimized feature set. Finally, one dialog act is chosen based on the results provided by the binary classifiers. The reliability of the judgment of the binary classifiers is also considered. Results of experiments showed that our proposed method significantly outperformed a baseline that was trained using a single feature set.