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.
|出版狀態||Published - 2009|
|事件||21st Conference on Computational Linguistics and Speech Processing, ROCLING 2009 - Taichung, Taiwan|
持續時間: 2009 9月 1 → 2009 9月 2
|Other||21st Conference on Computational Linguistics and Speech Processing, ROCLING 2009|
|期間||09-09-01 → 09-09-02|
All Science Journal Classification (ASJC) codes