BLEU Score — Technology Wiki

Overview

Direct Answer

BLEU (Bilingual Evaluation Understudy) is a quantitative metric that measures the correspondence between machine-generated text and one or more reference translations by comparing n-gram overlap. It produces a score between 0 and 1, where higher scores indicate closer alignment with reference text.

How It Works

The metric calculates the proportion of n-grams (sequences of 1 to 4 words) in the generated output that appear in the reference text(s), applying a brevity penalty to prevent artificially inflated scores from shorter translations. Precision is computed for each n-gram length, then combined using geometric averaging to produce a single composite score.

Why It Matters

BLEU enables rapid, reproducible evaluation of machine translation and text generation systems without requiring manual human assessment, significantly reducing evaluation costs and enabling continuous quality monitoring across translation pipelines and model iterations.

Common Applications

The metric is widely deployed in machine translation evaluation, multilingual natural language processing research, and quality assurance workflows for automated subtitle generation and cross-language content adaptation systems.

Key Considerations

BLEU scores correlate imperfectly with human judgement of translation quality and cannot detect semantic correctness or fluency; a single reference translation may penalise valid alternative phrasings, necessitating supplementary evaluation methods for comprehensive quality assessment.

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