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<Paper uid="H05-1012">
  <Title>Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 89-96, Vancouver, October 2005. c(c)2005 Association for Computational Linguistics A Maximum Entropy Word Aligner for Arabic-English Machine Translation</Title>
  <Section position="3" start_page="0" end_page="89" type="intro">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> Most of the prior work on word alignments has been done on parallel corpora where the alignment at the sentence level is also done automatically. The IBM models 1-5 (Brown et al., 1993) produce word alignments with increasing algorithmic complexity and performance. These IBM models and more recent refinements (Moore, 2004) as well as algorithms that bootstrap from these models like the HMM algorithm described in (Vogel et al., 1996) are unsupervised algorithms.</Paragraph>
    <Paragraph position="1"> The relative success of these automatic techniques together with the human annotation cost has delayed the collection of supervised word-aligned corpora for more than a decade.</Paragraph>
    <Paragraph position="2"> (Cherry and Lin, 2003) recently proposed a direct alignment formulation and state that it would be straightforward to estimate the parameters given a supervised alignment corpus. In this paper, we extend their work and show that with a small amount of annotated data, together with a modeling strategy and search algorithm yield significant gains in alignment F-measure.</Paragraph>
  </Section>
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