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<Paper uid="I05-2014">
  <Title>ATR - Spoken language communication research labs</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU or NIST, are now well established. Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese, because of the word segmentation problem. This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at the character level. The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs.</Paragraph>
  </Section>
class="xml-element"></Paper>