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<Paper uid="W00-0508">
  <Title>Stochastic Finite-State models for Spoken Language Machine ': anslation</Title>
  <Section position="3" start_page="52" end_page="52" type="intro">
    <SectionTitle>
2 Stochastic Machine Translation
</SectionTitle>
    <Paragraph position="0"> In machine translation, the objective is to map a source symbol sequence Ws = wx,...,WNs (wi E Ls) into a target sequence WT = xl,..., XNT (Xi E LT). The statistical machine translation approach is based on the noisy channel paradigm and the Maximum-A-Posteriori decoding algorithm (Brown et al., 1993). The sequence Ws is thought as a noisy version of WT and the best guess I)d~ is then computed as</Paragraph>
    <Paragraph position="2"> In (Brown et al., 1993) they propose a method for maximizing P(WTIWs) by estimating P(WT) and P(WsIWT) and solving the problem in equation 1.</Paragraph>
    <Paragraph position="3"> Our approach to statistical machine translation differs from the model proposed in (Brown et al., 1993) in that: * We compute the joint model P(Ws, WT) from the bilanguage corpus to account for the direct mapping of the source sentence Ws into the target sentence I?VT that is ordered according to the * source language word order. The target string IfV~ is then chosen from all possible reorderings 2 of</Paragraph>
    <Paragraph position="5"> where AT is the target language model and AWT are the different reorderings of WT.</Paragraph>
    <Paragraph position="6"> * We decompose the translation problem into local (phrase-level) and global (sentence-level) source-target string transduction.</Paragraph>
    <Paragraph position="7"> * We automatically learn stochastic automata and transducers to perform the sentence-level and phrase-level translation.</Paragraph>
    <Paragraph position="8"> As shown in Figure 1, the stochastic machine translation system consists of two phases, the lexical choice phase and the reordering phase. In the next sections we describe the finite-state machine components and the operation cascade that implements this translation algorithm.</Paragraph>
  </Section>
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