BLEU Score: Judging Translation by Human Overlap
The BLEU score judges a machine translation by how closely its text matches a professional human translation. It's a popular, automated, and inexpensive way to benchmark translation systems, like comparing different versions of a model. The main footgun is that a high score indicates high textual overlap, not necessarily better fluency or meaning, as it's just a proxy for human judgment.
Think of the BLEU score as an automated judge for machine translation, working from the principle that "the closer a machine translation is to a professional human translation, the better it is." It provides a fast, inexpensive way to evaluate quality, making it a standard metric for comparing different translation models. Since its invention at IBM in 2001, it has remained popular due to its high correlation with human quality judgments. The key footgun is mistaking this textual 'correspondence' for true semantic quality; a translation can use different but valid words and get a lower score.
Read the original → Wikipedia: BLEU
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