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<Paper uid="H05-1022">
  <Title>Machine Intelligence Lab, Cambridge University Engineering Department</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> HMM-based models are developed for the alignment of words and phrases in bitext.</Paragraph>
    <Paragraph position="1"> The models are formulated so that alignment and parameter estimation can be performed efficiently. We find that Chinese-English word alignment performance is comparable to that of IBM Model-4 even over large training bitexts. Phrase pairs extracted from word alignments generated under the model can also be used for phrase-based translation, and in Chinese to English and Arabic to English translation, performance is comparable to systems based on Model-4 alignments. Direct phrase pair induction under the model is described and shown to improve translation performance.</Paragraph>
  </Section>
class="xml-element"></Paper>