Dialogue act detection using sentence structure and partial pattern trees

This paper presents a dialogue act detection approach using sentence structures and partial pattern trees to generate candidate sentences (CSs). A syntactic parser is utilized to convert the CSs to sentence grammar rules (SRs). To avoid the confusion between dialogue intentions, the K-means algorithm is adopted to cluster the sentence structures of the same dialogue intention based on the SRs. Finally, the relationship between these SRs and the intentions is modeled by a latent dialogue act matrix. Moreover, for the application to a travel information dialogue system, optimal dialogue strategies are trained using the partially observable Markov decision process (POMDP) for robust dialogue management. In evaluation, compared to the semantic slot-based method which achieves 48.1% dialogue act detection accuracy, the proposed approach can achieve 81.9% accuracy, with 33.3% improvement.