Calculator
Example Data Table
| Case | Candidate | Reference 1 | Reference 2 | Expected Insight |
|---|---|---|---|---|
| Example A | the cat is on the mat | there is a cat on the mat | this cat is sitting on the mat | Good unigram overlap with moderate longer-phrase mismatch. |
| Example B | machine translation needs careful evaluation | careful evaluation is needed for machine translation | machine translation requires thoughtful evaluation | Word overlap exists, but word order affects higher n-grams. |
| Example C | a short answer | this is a much longer expected answer | the reference response is longer and richer | Brevity penalty reduces the final score. |
Formula Used
BLEU combines clipped n-gram precision with a brevity penalty. It rewards overlap with reference text while discouraging overly short candidate outputs.
Clipped Precision:
pn = (sum of clipped candidate n-gram matches) / (sum of candidate n-grams)
Brevity Penalty:
BP = 1, when c > r
BP = exp(1 - r / c), when c ≤ r
Final BLEU:
BLEU = BP × exp( Σ wn log(pn) )
Where:
This calculator supports custom weights, several smoothing options, multiple references, punctuation handling, and case-sensitive or normalized evaluation.
How to Use This Calculator
- Paste the generated candidate sentence or translation into the candidate field.
- Add one or more reference sentences, with each reference on a separate line.
- Choose the highest n-gram order you want to include, usually up to four.
- Set custom weights if you need non-uniform importance across n-gram levels.
- Select a smoothing method to avoid zero precision collapse on sparse matches.
- Enable case sensitivity or punctuation removal based on your evaluation policy.
- Click the calculate button to show the result above the form.
- Review the summary, precision table, and chart, then export the result as CSV or PDF.
Frequently Asked Questions
1. What does BLEU measure?
BLEU measures overlap between a candidate output and one or more references. It emphasizes n-gram precision and adjusts the final score when the candidate is much shorter than expected.
2. Why can a fluent sentence receive a low BLEU score?
BLEU rewards surface overlap. A fluent paraphrase may use different wording or word order than the references, which lowers higher-order n-gram precision even when the meaning stays correct.
3. Why does the brevity penalty matter?
Without brevity penalty, very short candidates could score too well by matching only a few common words. The penalty discourages incomplete outputs and makes scores more realistic.
4. When should I use smoothing?
Use smoothing when short texts or sparse matches produce zero counts in higher n-grams. Smoothing prevents the whole BLEU score from collapsing to zero too easily.
5. Can I compare multiple references?
Yes. Multiple references usually improve fairness because they capture valid wording alternatives. The calculator clips candidate counts against the maximum count observed across the references.
6. Should punctuation and letter case be normalized?
That depends on your evaluation policy. Normalization reduces accidental mismatches from formatting differences, while case-sensitive scoring is stricter and may suit specialized text comparison tasks.
7. Is BLEU enough for model evaluation?
BLEU is useful, but not complete. It should be combined with human review or meaning-focused metrics because overlap alone does not fully capture adequacy, fluency, or factual correctness.
8. What is a good BLEU score?
There is no universal threshold. A good score depends on domain, task difficulty, tokenization policy, reference quality, and text length. Compare scores only under consistent evaluation settings.