Calculation Results
N-Gram Analysis
| Order | Candidate N-Grams | Reference N-Grams | Clipped Matches | Total | Modified Precision | Weight | Weighted Log |
|---|
Sentence-Level Corpus Breakdown
| # | Candidate | BLEU | Candidate Length | Reference Length | Status |
|---|
Candidate Comparison and Ranking
| Rank | Candidate | BLEU | Percentage |
|---|
Token Match Highlight
Green tokens appear in at least one reference. Red tokens do not.
Complete Calculation Steps
Recent Calculation History
Formula Used
Here, BP is the brevity penalty. The value pn is modified n-gram precision. Candidate length is c, while effective reference length is r.
How to Use
- Select sentence or corpus evaluation.
- Enter a candidate and one reference minimum.
- Choose n-gram, preprocessing, and smoothing settings.
- Set reference length and precision preferences.
- Calculate, inspect diagnostics, then export results.
Example Data
| Type | Text |
|---|---|
| Candidate | the cat is on the mat |
| Reference 1 | the cat is sitting on the mat |
| Reference 2 | there is a cat on the mat |
Understanding BLEU
BLEU measures lexical overlap between generated and reference text. It compares n-grams after clipping repeated matches. Higher scores usually indicate closer wording.
Modified precision limits matches using reference frequencies. The brevity penalty discourages overly short candidates. Corpus BLEU is generally more stable.
Smoothing prevents zero higher-order precision from collapsing scores. Different methods can produce different sentence scores. Report settings with every evaluation.
Frequently Asked Questions
1. What does BLEU measure?
BLEU measures n-gram overlap between generated text and references. It rewards shared phrases and penalizes short outputs. It does not directly measure meaning.
2. Is a higher BLEU score always better?
A higher score usually means stronger lexical agreement. Quality still depends on references and domain. Human evaluation remains important for nuanced outputs.
3. Why can sentence BLEU become zero?
Missing one higher-order n-gram can create zero precision. The geometric mean then collapses the score. Smoothing reduces this effect for short sentences.
4. What is clipped precision?
Clipped precision limits repeated candidate matches. Reference counts define the maximum accepted frequency. This prevents repetition from inflating performance.
5. Why use multiple references?
Multiple references capture more valid wording choices. They improve coverage of acceptable translations. Scores become less dependent on one phrasing.
6. What does the brevity penalty do?
The brevity penalty discourages very short candidates. It applies when candidate length is smaller. Longer candidates receive no brevity reduction.
7. Which BLEU order should I choose?
BLEU-4 is a common general choice. Lower orders focus more on local word overlap. Higher orders require longer exact phrase matches.
8. Should punctuation be removed?
That depends on your evaluation protocol. Consistent preprocessing matters more than one choice. Document every transformation applied before scoring.
9. Can BLEU compare summaries or captions?
BLEU can compare many generated text tasks. It works best with suitable references. Semantic metrics may complement lexical evaluation.