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<Paper uid="P06-2120">
  <Title>Stochastic Discourse Modeling in Spoken Dialogue Systems Using Semantic Dependency Graphs</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This investigation proposes an approach to modeling the discourse of spoken dialogue using semantic dependency graphs.</Paragraph>
    <Paragraph position="1"> 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 taskcompletion 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. null</Paragraph>
  </Section>
class="xml-element"></Paper>