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<Paper uid="H05-1095">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 755-762, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics Translating with non-contiguous phrases</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Possibly the most remarkable evolution of recent years in statistical machine translation is the step from word-based models to phrase-based models (Och et al., 1999; Marcu and Wong, 2002; Yamada and Knight, 2002; Tillmann and Xia, 2003). While in traditional word-based statistical models (Brown et al., 1993) the atomic unit that translation operates on is the word, phrase-based methods acknowledge the significant role played in language by multi-word expressions, thus incorporating in a statistical framework the insight behind Example-Based Machine Translation (Somers, 1999).</Paragraph>
    <Paragraph position="1"> However, Phrase-based models proposed so far only deal with multi-word units that are sequences of contiguous words on both the source and the target side. We propose here a model designed to deal with multi-word expressions that need not be contiguous in either or both the source and the target side.</Paragraph>
    <Paragraph position="2"> The rest of this paper is organised as follows. Section 2 provides motivations, definition and extraction procedure for non-contiguous phrases. The log-linear conditional translation model we adopted is the object of Section 3; the method used to train its parameters is described in Section 4. Section 5 briefly describes the decoder. The experiments we conducted to asses the effectiveness of using non-contiguous phrases are presented in Section 6.</Paragraph>
  </Section>
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