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<Paper uid="W06-3120">
  <Title>Adri a de Gispert</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Nowadays most Statistical Machine Translation (SMT) systems use phrases as translation units. In addition, the decision rule is commonly modelled through a log-linear maximum entropy framework which is based on several feature functions (including the translation model), hm. Each feature function models the probability that a sentence e in the target language is a translation of a given sentence f in the source language. The weights, li, of each feature function are typically optimized to maximize a scoring function. It has the advantage that additional features functions can be easily integrated in the overall system.</Paragraph>
    <Paragraph position="1"> This paper describes a Phrase-Based system whose baseline is similar to the system in Costa-juss a and Fonollosa (2005). Here we introduce two reordering approaches and add morphological information. Translation results for all six translation directions proposed in the shared task are presented and discussed. More speci cally, four different languages are considered: English (en), Spanish (es), French (fr) and German (de); and both translation directions are considered for the pairs: EnEs, EnFr, and EnDe. The paper is organized as follows: Section 2 describes the system; 0This work has been supported by the European Union under grant FP6-506738 (TC-STAR project) and the TALP Research Center (under a TALP-UPC-Recerca grant).</Paragraph>
    <Paragraph position="2"> Section 3 presents the shared task results; and, nally, in Section 4, we conclude.</Paragraph>
  </Section>
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