| tech,31-4-I05-2014,bq | texts </term> with , for instance , <term> | statistical MT systems | </term> which usually segment their <term> | #7777 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. |
| measure(ment),13-1-I05-2014,bq | Translation ( MT ) systems </term> , such as <term> | BLEU | </term> or <term> NIST </term> , are now well | #7691 Automatic evaluation metrics for Machine Translation (MT) systems, such asBLEU or NIST, are now well established. |
| measure(ment),1-1-I05-2014,bq | translation output </term> . Automatic <term> | evaluation metrics | </term> for <term> Machine Translation ( MT | #7679 Automaticevaluation metrics for Machine Translation (MT) systems, such as BLEU or NIST, are now well established. |
| tech,22-4-I05-2014,bq | possible to directly compare commercial <term> | systems | </term> outputting <term> unsegmented texts | #7768 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercialsystems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs. |
| tech,4-1-I05-2014,bq | <term> evaluation metrics </term> for <term> | Machine Translation ( MT ) systems | </term> , such as <term> BLEU </term> or <term> | #7682 Automatic evaluation metrics forMachine Translation ( MT ) systems, such as BLEU or NIST, are now well established. |
| other,12-3-I05-2014,bq | standard use of <term> BLEU </term> in <term> | word n-grams | </term> and its application at the <term> character | #7736 This study establishes the equivalence between the standard use of BLEU inword n-grams and its application at the character level. |
| other,13-2-I05-2014,bq | of <term> language pairs </term> like <term> | English-Chinese | </term> or <term> English-Japanese </term> , | #7713 Yet, they are scarcely used for the assessment of language pairs likeEnglish-Chinese or English-Japanese, because of the word segmentation problem. |
| other,10-2-I05-2014,bq | scarcely used for the assessment of <term> | language pairs | </term> like <term> English-Chinese </term> or | #7710 Yet, they are scarcely used for the assessment oflanguage pairs like English-Chinese or English-Japanese, because of the word segmentation problem. |
| measure(ment),10-3-I05-2014,bq | equivalence between the standard use of <term> | BLEU | </term> in <term> word n-grams </term> and its | #7734 This study establishes the equivalence between the standard use ofBLEU in word n-grams and its application at the character level. |
| measure(ment),3-4-I05-2014,bq | character </term> level . The use of <term> | BLEU | </term> at the <term> character </term> level | #7749 The use ofBLEU 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. |
| measure(ment),15-1-I05-2014,bq | </term> , such as <term> BLEU </term> or <term> | NIST | </term> , are now well established . Yet | #7693 Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU orNIST, are now well established. |
| other,15-2-I05-2014,bq | like <term> English-Chinese </term> or <term> | English-Japanese | </term> , because of the <term> word segmentation | #7715 Yet, they are scarcely used for the assessment of language pairs like English-Chinese orEnglish-Japanese, because of the word segmentation problem. |
| other,24-4-I05-2014,bq | commercial <term> systems </term> outputting <term> | unsegmented texts | </term> with , for instance , <term> statistical | #7770 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputtingunsegmented texts with, for instance, statistical MT systems which usually segment their outputs. |
| other,19-3-I05-2014,bq | n-grams </term> and its application at the <term> | character | </term> level . The use of <term> BLEU </term> | #7743 This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at thecharacter level. |
| other,6-4-I05-2014,bq | The use of <term> BLEU </term> at the <term> | character | </term> level eliminates the <term> word segmentation | #7752 The use of BLEU at thecharacter 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. |
| other,10-4-I05-2014,bq | character </term> level eliminates the <term> | word segmentation problem | </term> : it makes it possible to directly | #7756 The use of BLEU at the character level eliminates theword 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. |
| other,20-2-I05-2014,bq | English-Japanese </term> , because of the <term> | word segmentation problem | </term> . This study establishes the equivalence | #7720 Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese, because of theword segmentation problem. |
| other,38-4-I05-2014,bq | </term> which usually segment their <term> | outputs | </term> . We present the first known <term> | #7784 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 theiroutputs. |
