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<Paper uid="P05-3026">
  <Title>Multi-Engine Machine Translation Guided by Explicit Word Matching</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We describe a new approach for synthetically combining the output of several different Machine Translation (MT) engines operating on the same input. The goal is to produce a synthetic combination that surpasses all of the original systems in translation quality. Our approach uses the individual MT engines as &amp;quot;black boxes&amp;quot; and does not require any explicit cooperation from the original MT systems. A decoding algorithm uses explicit word matches, in conjunction with confidence estimates for the various engines and a tri-gram language model in order to score and rank a collection of sentence hypotheses that are synthetic combinations of words from the various original engines.</Paragraph>
    <Paragraph position="1"> The highest scoring sentence hypothesis is selected as the final output of our system. Experiments, using several Arabic-to-English systems of similar quality, show a substantial improvement in the quality of the translation output.</Paragraph>
  </Section>
class="xml-element"></Paper>