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<Paper uid="W05-0801">
  <Title>Identifying Word Correspondences in Parallel Texts. In Proceedings of the Speech and Natural</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Bilingual word alignment forms the foundation of current work on statistical machine translation. Standard word-alignment methods involve the use of probabilistic generative models that are complex to implement and slow to train.</Paragraph>
    <Paragraph position="1"> In this paper we show that it is possible to approach the alignment accuracy of the standard models using algorithms that are much faster, and in some ways simpler, based on basic word-association statistics.</Paragraph>
  </Section>
class="xml-element"></Paper>