Dialogue act detection using sentence structure and partial pattern trees

Wei Bin Liang, Yu Cheng Hsiao, Chung Hsien Wu

研究成果: Paper同行評審

摘要

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.

原文English
頁面223-236
頁數14
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 12009 9月 2

Other

Other21st Conference on Computational Linguistics and Speech Processing, ROCLING 2009
國家/地區Taiwan
城市Taichung
期間09-09-0109-09-02

All Science Journal Classification (ASJC) codes

  • 語言與語言學
  • 言語和聽力

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