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<Paper uid="W02-1012">
  <Title>Extensions to HMM-based Statistical Word Alignment Models</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper we presented three extensions to HMM-based alignment models. We showed that incorporating part of speech tag information of the source and target languages in the translation model improves word alignment accuracy. We also presented a method for approximately modeling fertility in an HMM-based model and a new generative model for target language words that do not have correspondences in the source language. The proposed models do not increase significantly the complexity of the learning algorithms while providing a better account for some phenomena in natural language translation.</Paragraph>
  </Section>
class="xml-element"></Paper>