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<Paper uid="C04-1022">
  <Title>Automatic Learning of Language Model Structure</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented a data-driven approach to the selection of parameters determining the structure and performance of factored language models, a class of models which generalizes standard language models by including additional conditioning variables in a principled way. In addition to reductions in perplexity obtained by FLMs vs. standard language models, the data-driven model section method further improved perplexity and outperformed both knowledge-based manual search and random search.</Paragraph>
  </Section>
class="xml-element"></Paper>