Stochastic Discourse Modeling in Spoken Dialogue Systems Using Semantic Dependency Graphs

Jui-Feng Yeh, Chung-Hsien Wu, Mao-Zhu Yang

Abstract: 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.