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<Paper uid="P99-1049">
  <Title>An Efficient Statistical Speech Act Type Tagging System for Speech Translation Systems</Title>
  <Section position="10" start_page="387" end_page="387" type="concl">
    <SectionTitle>
8 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have described a new efficient statistical speech act type tagging system based on a statistical model used in Japanese morphological analyzers. This system integrates linguistic, acoustic, and situational features and efficiently performs optimal segmentation of a turn and tagging. From several tagging experiments, we showed that the system segmented turns and assigned speech act type tags at high accuracy rates when using Japanese data. Comparatively lower performance was obtained using English data, and we discussed the performance difference.</Paragraph>
    <Paragraph position="1"> We Mso examined the effect of parameters in the statistical models on tagging performance. We finally showed that the SA tags in this paper are useful in translating positive responses that often appear in task-oriented dialogues such as those in ours.</Paragraph>
  </Section>
class="xml-element"></Paper>