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<Paper uid="C96-2141">
  <Title>HMM-Based Word Alignment in Statistical Translation</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In this paper, we address the problem of word alignments for a bilingual corpus. In the recent years, there have been a number of papers considering this or similar problems: (Brown et al., 1990), (Dagan et al., 1993), (Kay et al., 1993), (Fung et al., 1993).</Paragraph>
    <Paragraph position="1"> In our approach, we use a first-order Hidden Markov model (HMM) (aelinek, 1976), which is similar, but not identical to those used in speech recognition. The key component of this approach is to make the alignment probabilities dependent not on the absolute position of the word alignment, but on its relative position; i.e. we consider the differences in the index of the word positions rather than the index itself.</Paragraph>
    <Paragraph position="2"> The organization of the paper is as follows.</Paragraph>
    <Paragraph position="3"> After reviewing the statistical approach to machine translation, we first describe the conventional model (mixture model). We then present our first-order HMM approach in lull detail. Finally we present some experimental results and compare our model with the conventional model.</Paragraph>
  </Section>
class="xml-element"></Paper>