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<Paper uid="W06-3125">
  <Title>Adri a de Gispert</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The statistical machine translation approach used in this work implements a log-linear combination of feature functions along with a translation model which is based on bilingual n-grams (de Gispert and Mari no, 2002).</Paragraph>
    <Paragraph position="1"> This translation model differs from the well known phrase-based translation approach (Koehn et al., 2003) in two basic issues: rst, training data is monotonously segmented into bilingual units; and second, the model considers n-gram probabilities instead of relative frequencies. This translation approach is described in detail in (Mari no et al., 2005). For those translation tasks with Spanish or English as target language, an additional tagged (using POS information) target language model is used. Additionally a reordering strategy that includes POS information is described and evaluated.</Paragraph>
    <Paragraph position="2"> Translation results for all six translation directions proposed in the shared task are presented and discussed. Both translation directions are considered for the pairs: English-Spanish, English-French, and English-German.</Paragraph>
    <Paragraph position="3"> The paper is structured as follows: Section 2 brie y outlines the baseline system. Section 3 describes in detail the implemented POS-based re-ordering strategy. Section 4 presents and discusses the shared task results and, nally, section 5 presents some conclusions and further work.</Paragraph>
  </Section>
class="xml-element"></Paper>