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<Paper uid="H01-1055">
  <Title>RealizerSentencePlannerText Manager Dialog Natural Language Generation Planner Prosody Utterance User Utterance System Assigner TTS Natural Language Understanding ASR</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
ABSTRACT
</SectionTitle>
    <Paragraph position="0"> Recent advances in Automatic Speech Recognition technology have put the goal of naturally sounding dialog systems within reach.</Paragraph>
    <Paragraph position="1"> However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user. The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.</Paragraph>
  </Section>
class="xml-element"></Paper>