Stochastic discourse modeling in spoken dialogue systems using semantic dependency graphs

Jui Feng Yeh, Chung Hsien Wu, Mao Zhu Yang

研究成果: Paper同行評審

3 引文 斯高帕斯(Scopus)

摘要

This investigation proposes an approach to modeling the discourse of spoken dialogue using semantic dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user's utterance using the semantic dependency graphs. Dependency relation between the headword and other words in a sentence is detected using the semantic dependency grammar. In order to evaluate the proposed method, a dialogue system for medical service is developed. Experimental results show that the rates for speech act detection and task-completion are 95.6% and 85.24%, respectively, and the average number of turns of each dialogue is 8.3. Compared with the Bayes' classifier and the Partial-Pattern Tree based approaches, we obtain 14.9% and 12.47% improvements in accuracy for speech act identification, respectively.

原文English
頁面937-944
頁數8
出版狀態Published - 2006
事件21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, Australia
持續時間: 2006 7月 172006 7月 18

Conference

Conference21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
國家/地區Australia
城市Sydney
期間06-07-1706-07-18

All Science Journal Classification (ASJC) codes

  • 電腦繪圖與電腦輔助設計
  • 電腦視覺和模式識別
  • 建模與模擬
  • 人機介面

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