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<Paper uid="N04-1005">
  <Title>Balancing Data-driven and Rule-based Approaches in the Context of a Multimodal Conversational System</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we have addressed how to rapidly prototype multimodal conversational systems without relying on the collection of domain-specific corpora. We have presented several techniques that exploit domain-specific grammars, reuse out-of-domain corpora and adapt large conversational corpora and wide-coverage grammars to derive a domain-specific corpus. We have demonstrated that a language model trained on a derived corpus performs within 10% word accuracy of a language model trained on collected domain-specific corpus, suggesting a method of building an initial language model without having to collect domain-specific corpora. We have also presented and evaluated pattern-matching and classification-based approaches to improve the robustness of multimodal understanding. We have presented results for these approaches in the context of a multimodal city guide application (MATCH).</Paragraph>
  </Section>
class="xml-element"></Paper>