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BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU.
The quality of a translation is inherently subjective, there is no objective or quantifiable "good." Therefore, any metric must assign quality scores so they correlate with the human judgment of quality. That is, a metric should score highly translations that humans score highly, and give low scores to those humans give low scores.
As with BLEU, the basic unit of evaluation is the sentence, the algorithm first creates an alignment (see illustrations) between two sentences, the candidate translation string, and the reference translation string.
Koehn uses the BLEU metric by Papineni et al. (2002) for this, which counts the coincidences of the two compared versions—SMT output and corpus data—and calculates a score on this basis. [4] The more similar the two versions are, the higher the score, and therefore the quality of the translation. [1]
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Credit score requirement. Typically 620+, but 740+ for the best rates. As low as 300, but 670+ for best rates. Repayment terms. 5 to 30 years. 2 to 12 years. Loan amounts. Up to 85% of equity.
The quality of Microsoft Translator's machine translation outputs are evaluated using a method called the BLEU score. [11] BLEU (Bilingual Evaluation Understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a ...
As Super Bowl 59 approaches, fans are asking various questions about the event, including what the blue tent on the sideline during NFL games is used for.. The blue tent on the sidelines plays an ...