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<Paper uid="P06-2103">
  <Title>Discourse Generation Using Utility-Trained Coherence Models</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We describe a generic framework for integrating various stochastic models of discourse coherence in a manner that takes advantage of their individual strengths. An integral part of this framework are algorithms for searching and training these stochastic coherence models. We evaluate the performance of our models and algorithms and show empirically that utility-trained log-linear coherence models out-perform each of the individual coherence models considered.</Paragraph>
  </Section>
class="xml-element"></Paper>