Paper: Stochastic Discourse Modeling In Spoken Dialogue Systems Using Semantic Dependency Graphs

ACL ID P06-2120
Title Stochastic Discourse Modeling In Spoken Dialogue Systems Using Semantic Dependency Graphs
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

This investigation proposes an approach to modeling the discourse of spoken dia- logue using semantic dependency graphs. By characterizing the discourse as a se- quence 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 dia- logue system for medical service is devel- oped. Experimental results show that the rates for speech act detection and task- completion are 95.6% and 85.24%, re- spectively, and the average number of turns of each dialogue is 8.3. Comp...