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5 min readApr 23, 2025
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Machine translation has come a long way, from clunky rule-based systems to sleek neural models like Transformers. But how do we know if a machine’s translation is any good? Enter the BLEU score — a go-to metric for evaluating machine translation quality. Short for Bilingual Evaluation Understudy, BLEU is like a judge that compares a machine’s output to human translations. In this Medium blog, we’ll break down what BLEU is, dive into its math (don’t worry, it’s manageable!), and walk through a hands-on example with Python code. Whether you’re a data scientist, an NLP enthusiast, or just curious, this guide will make BLEU crystal clear.
What is the BLEU Score?
Imagine you’ve built…
Member-only story
5 min readApr 23, 2025
–
Press enter or click to view image in full size
Machine translation has come a long way, from clunky rule-based systems to sleek neural models like Transformers. But how do we know if a machine’s translation is any good? Enter the BLEU score — a go-to metric for evaluating machine translation quality. Short for Bilingual Evaluation Understudy, BLEU is like a judge that compares a machine’s output to human translations. In this Medium blog, we’ll break down what BLEU is, dive into its math (don’t worry, it’s manageable!), and walk through a hands-on example with Python code. Whether you’re a data scientist, an NLP enthusiast, or just curious, this guide will make BLEU crystal clear.
What is the BLEU Score?
Imagine you’ve built a model that translates English to French. You feed it “The cat is on the mat,” and it spits out “Le chat est sur le tapis.” Looks good, but is it really good? BLEU helps answer that by measuring how closely the machine’s translation (the “candidate”) matches one or more human translations (the “references”).
BLEU works by:
- Counting n-grams (sequences of 1, 2, 3, or 4 words) that appear in both the candidate and reference translations.
- Calculating precision — the fraction of n-grams in the candidate that match the reference.