Why is BLEU used to evaluate translation quality?
BLEU (Bilingual Evaluation Understudy) is a widely adopted automatic metric for evaluating the quality of machine translation (MT) output. Its core function is to quantify the similarity between a machine-generated translation and high-quality reference translations provided by humans.
BLEU calculates its score primarily based on n-gram precision, comparing how many contiguous word sequences (n-grams) in the MT output appear in the reference translations. It penalizes overly short outputs via a brevity factor. While efficient and objective for large-scale evaluation, BLEU has limitations. It relies heavily on surface-level matches and often correlates best with fluency rather than deep semantic adequacy. Its performance can be sensitive to the number and quality of reference translations used and may struggle with valid alternative rephrasing not present in the references.
The primary value of BLEU lies in its automation, speed, cost-effectiveness, and consistency. This enables rapid iteration and comparison of different MT systems or configurations during research, development, and deployment phases. It provides a standardized benchmark for tracking improvements in translation models over time, despite its known correlation weaknesses with human judgments of meaning and naturalness.
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