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<Paper uid="P97-1037">
  <Title>Transformation Step Original CorPora + Categorization + 'por2favor ' + Word Splitting Translation Errors \[~.\]</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we describe a Dynamic Programming (DP) based search algorithm for statistical translation and present experimental results. The statistical translation uses two sources of information: a translation model and a language model. The language model used is a standard bigram model. For the translation lnodel, the alignment probabilities are made dependent on the differences in the alignment positions rather than on the absolute positions. Thus, the approach amounts to a first-order Hidden Markov model (HMM) as they are used successfully in speech recognition for the time alignment problem. Under the assumption that the alignment is monotone with respect to the word order in both languages, an efficient search strategy for translation can be formulated. The details of the search algorithm are described. Experiments on the EuTrans corpus produced a word error rate of 5.1(/~..</Paragraph>
  </Section>
class="xml-element"></Paper>