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<Paper uid="W06-3117">
  <Title>Stochastic Inversion Transduction Grammars for Obtaining Word Phrases for Phrase-based Statistical Machine Translation</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Phrase-based statistical translation systems are currently providing excellent results in real machine translation tasks (Zens et al., 2002; Och and Ney, 2003; Koehn, 2004). In phrase-based statistical translation systems, the basic translation units are word phrases.</Paragraph>
    <Paragraph position="1"> An important problem that is related to phrase-based statistical translation is to automatically obtain bilingual word phrases from parallel corpora.</Paragraph>
    <Paragraph position="2"> Several methods have been defined for dealing with this problem (Och and Ney, 2003). In this work, we study a method for obtaining word phrases that is based on Stochastic Inversion Transduction Grammars that was proposed in (Wu, 1997).</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Stochastic Inversion Transduction Grammars
</SectionTitle>
      <Paragraph position="0"> (SITG) can be viewed as a restricted Stochastic Context-Free Syntax-Directed Transduction Scheme. SITGs can be used to carry out a simultaneous parsing of both the input string and the output string. In this work, we apply this idea to obtain aligned word phrases to be used in phrase-based translation systems (Sanchez and Benedi, 2006).</Paragraph>
      <Paragraph position="1"> In Section 2, we review the phrase-based machine translation approach. SITGs are reviewed in Section 3. In Section 4, we present experiments on the shared task proposed in this workshop with the Europarl corpus.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>