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<Paper uid="W05-1601">
  <Title>Statistical Generation: Three Methods Compared and Evaluated</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Statistical NLG has largely meant n-gram modelling which has the considerable advantages of lending robustness to NLG systems, and of making automatic adaptation to new domains from raw corpora possible. On the downside, n-gram models are expensive to use as selection mechanisms and have a built-in bias towards shorter realisations. This paper looks at treebank-training of generators, an alternative method for building statistical models for NLG from raw corpora, and two different ways of using treebank-trained models during generation.</Paragraph>
    <Paragraph position="1"> Results show that the treebank-trained generators achieve improvements similar to a 2-gram generator over a baseline of random selection. However, the treebank-trained generators achieve this at a much lower cost than the 2-gram generator, and without its strong preference for shorter realisations. null</Paragraph>
  </Section>
class="xml-element"></Paper>