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<Paper uid="P05-1068">
  <Title>Context-dependent SMT Model using Bilingual Verb-Noun Collocation</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
BD
</SectionTitle>
    <Paragraph position="0"> B5. The translation model links the source language sentence to the target language sentence. The language model describes the well-formedness of the target language sentence and might play a role in restricting hypothesis expansion during decoding.</Paragraph>
    <Paragraph position="1"> To recover the word order difference between two languages, it also allows modeling the reordering by introducing a relative distortion probability distribution. However, in spite of using such a language model and a distortion model, the translation outputs may not be fluent or in fact may produce nonsense.</Paragraph>
    <Paragraph position="2"> To make things worse, the huge hypothesis search space is much too large for an exhaustive search. If arbitrary reorderings are allowed, the search problem is NP-complete (Knight, 1999). According to a previous analysis (Koehn et al., 2004) of how many hypotheses are generated during an exhaustive search using the IBM models, the upper bound for the number of states is estimated by C6 B3 BE</Paragraph>
  </Section>
class="xml-element"></Paper>