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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="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Moderate-sized rule-based spoken language models for recognition and understanding are easy to develop and provide the ability to rapidly prototype conversational applications.</Paragraph>
    <Paragraph position="1"> However, scalability of such systems is a bottleneck due to the heavy cost of authoring and maintenance of rule sets and inevitable brittleness due to lack of coverage in the rule sets. In contrast, data-driven approaches are robust and the procedure for model building is usually simple. However, the lack of data in a particular application domain limits the ability to build data-driven models. In this paper, we address the issue of combining data-driven and grammar-based models for rapid prototyping of robust speech recognition and understanding models for a multimodal conversational system. We also present methods that reuse data from different domains and investigate the limits of such models in the context of a particular application domain.</Paragraph>
  </Section>
class="xml-element"></Paper>