Also known as: Bilingual Evaluation Understudy (BLEU) Β· BLEU
Bilingual Evaluation Understudy (BLEU) is a metric for evaluating machine translation by comparing machine-generated text with one or more human reference translations. It primarily measures n-gram overlap and is useful for corpus-level comparison, though it may not fully capture meaning, fluency, or acceptable paraphrase.
A metric between 0.0 and 1.0 for evaluating machine translations, for example, from Spanish to Japanese. To calculate a score, BLEU typically compares an ML model's translation (generated text) to a human expert's translation (reference text). The degree to which N-grams in the generated text and reference text match determines the BLEU score. The original paper on this metric is BLEU: a Method for Automatic Evaluation of Machine Translation. See also BLEURT.