measure(ment),0-1-I05-2014,ak <term> syntactic analysis system </term> . <term> Automatic evaluation metrics </term> for <term> Machine Translation ( MT
tech,4-1-I05-2014,ak Automatic evaluation metrics </term> for <term> Machine Translation ( MT ) systems </term> , such as <term> BLEU </term> or <term>
measure(ment),13-1-I05-2014,ak Translation ( MT ) systems </term> , such as <term> BLEU </term> or <term> NIST </term> , are now well
measure(ment),15-1-I05-2014,ak </term> , such as <term> BLEU </term> or <term> NIST </term> , are now well established . Yet
other,10-2-I05-2014,ak scarcely used for the assessment of <term> language pairs </term> like English-Chinese or English-Japanese
other,20-2-I05-2014,ak English-Japanese , because of the <term> word segmentation problem </term> . This study establishes the equivalence
measure(ment),10-3-I05-2014,ak equivalence between the standard use of <term> BLEU </term> in <term> word n-grams </term> and its
other,12-3-I05-2014,ak standard use of <term> BLEU </term> in <term> word n-grams </term> and its application at the <term> character
other,19-3-I05-2014,ak n-grams </term> and its application at the <term> character level </term> . The use of <term> BLEU </term> at the
measure(ment),3-4-I05-2014,ak character level </term> . The use of <term> BLEU </term> at the <term> character level </term>
other,6-4-I05-2014,ak The use of <term> BLEU </term> at the <term> character level </term> eliminates the <term> word segmentation
other,10-4-I05-2014,ak character level </term> eliminates the <term> word segmentation problem </term> : it makes it possible to directly
tech,31-4-I05-2014,ak unsegmented texts with , for instance , <term> statistical MT systems </term> which usually segment their outputs
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