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<Paper uid="P98-2221">
  <Title>Modeling with Structures in Statistical Machine Translation</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Most statistical machine translation systems employ a word-based alignment model. In this paper we demonstrate that word-based alignment is a major cause of translation errors. We propose a new alignment model based on shallow phrase structures, and the structures can be automatically acquired from parallel corpus.</Paragraph>
    <Paragraph position="1"> This new model achieved over 10% error reduction for our spoken language translation task.</Paragraph>
  </Section>
class="xml-element"></Paper>