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<Paper uid="P06-2120">
  <Title>Stochastic Discourse Modeling in Spoken Dialogue Systems Using Semantic Dependency Graphs</Title>
  <Section position="6" start_page="943" end_page="943" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper has presented a semantic dependency graph that robustly and effectively deals with a variety of conversational discourse information in the spoken dialogue systems. By modeling the dialogue discourse as the speech act sequence, the predictive method for speech act identification is proposed based on discourse analysis instead of keywords only. According to the corpus analysis, we can find the model proposed in this paper is practicable and effective. The results of the experiments show the semantic dependency graph outperforms those based on the Bayes' rule and partial pattern trees. By integrating discourse analysis this result also shows the improvement obtained not only in the identification rate of speech act but also in the performance for semantic object extraction.</Paragraph>
  </Section>
class="xml-element"></Paper>