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<Paper uid="W06-2606">
  <Title>Reranking Translation Hypotheses Using Structural Properties</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We investigate methods that add syntactically motivated features to a statistical machine translation system in a reranking framework. Thegoalistoanalyzewhether shallow parsing techniques help in identifying ungrammatical hypotheses. We show that improvements are possible by utilizing supertagging, lightweight dependency analysis, a link grammar parser and a maximum-entropy based chunk parser.</Paragraph>
    <Paragraph position="1"> Adding features to n-best lists and discriminatively training the system on a development set increases the BLEU score up to 0.7% on the test set.</Paragraph>
  </Section>
class="xml-element"></Paper>