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<Paper uid="P01-1056">
  <Title>Evaluating a Trainable Sentence Planner for a Spoken Dialogue System</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches. In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments. In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.</Paragraph>
  </Section>
class="xml-element"></Paper>