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<Paper uid="W00-0508">
  <Title>Stochastic Finite-State models for Spoken Language Machine ': anslation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Stochastic finite-state models are efficiently learnable from data, effective for decoding and are associated with a calculus for composing models which allows for tight integration of constraints frora various levels of language processing. In this paper, we present a method for stochastic finite-state machine translation that is trained automatically from pairs of source and target utterances. We use this method to develop models for English-Japanese and Japanese-English translation. We have embedded the Japanese-English translation system in a call routing task of unconstrained speech utterances. We evaluate the efficacy of the translation system :in the context of this application.</Paragraph>
  </Section>
class="xml-element"></Paper>