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BLEU-4 Score

HardEvaluation Metrics 📚 LLM 3: Applications & Evaluation

Compute the full BLEU-4 score.

BLEU-4 uses n-gram precisions for n=1,2,3,4: $\text{BLEU-4} = BP \cdot \exp\left(\sum_{n=1}^{4} \frac{1}{4} \log p_n\right)$

where p_n is the modified n-gram precision (clipped counts / candidate n-gram count) and BP is the brevity penalty.

If any p_n = 0, BLEU-4 = 0.

Input:

Line 1: reference
Line 2: candidate

Output: BLEU-4 score, rounded to 4 decimal places.

Example:

Input:

the cat is on the mat
the cat sat on the mat

Output:

0.6687

Reasoning:

First, we calculate the modified n-gram precisions ( $p_n$ $p_{n}$ ) for $n=1,2,3,4$ $n = 1, 2, 3, 4$ by comparing the reference and candidate sentences:
- $p_1 = \frac{\text{clipped unigram count}}{\text{candidate unigram count}} = \frac{7}{7} = 1$
- $p_2 = \frac{\text{clipped bigram count}}{\text{candidate bigram count}} = \frac{6}{6} = 1$
- $p_3 = \frac{\text{clipped trigram count}}{\text{candidate trigram count}} = \frac{5}{5} = 1$
- $p_4 = \frac{\text{clipped four-gram count}}{\text{candidate four-gram count}} = \frac{4}{4} = 1$ (for "the cat is on" and other similar sequences) and other sequences have matching counts as well, but "the cat sat" does not match "the cat is", so $p_4$ actually equals $\frac{4}{5}$ (one less four-gram match) which is $0.8$
Then, we apply the brevity penalty ( $BP$ ): since the candidate sentence has the same length as the reference sentence, $BP = 1$
Next, we compute the BLEU-4 score using the formula: $BLEU-4 = BP \cdot \exp\left(\sum_{n=1}^{4} \frac{1}{4} \log p_n\right) = 1 \cdot \exp\left(\frac{1}{4}(\log 1 + \log 1 + \log 1 + \log 0.8)\right)$
The final output is $1 \cdot \exp\left(\frac{1}{4}(\log 1 + \log 1 + \log 1 + \log 0.8)\right) = 1 \cdot \exp\left(\frac{1}{4}(\log 0.8)\right) = 1 \cdot \exp\left(\frac{1}{4} \cdot -0.2231435513\right) = 1 \cdot \exp(-0.055785388)\approx 0.668

Constraints:

If candidate has fewer than 4 tokens, some p_n will be 0 → BLEU = 0
Use geometric mean (equal weights 1/4 each)
Round to 4 decimal places

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Background Knowledge

The BLEU-4 score is a widely used evaluation metric in natural language processing (NLP) to measure the quality of machine translation systems. It calculates the similarity between a candidate translation and one or more reference translations. The score is based on the concept of n-grams, which are sequences of n items (in this case, words) from a given text. The BLEU-4 score uses n-gram precisions for n=1,2,3,4, providing a more comprehensive assessment of the translation quality.

The brevity penalty (BP) is a component of the BLEU-4 score that penalizes candidate translations that are shorter than the reference translations. This is because shorter translations may have a higher n-gram precision simply due to their length, rather than their actual quality. The brevity penalty ensures that the BLEU-4 score rewards translations that are both accurate and complete. The modified n-gram precision is another crucial concept, which calculates the ratio of clipped counts (the number of n-grams in the candidate translation that match the reference translation) to the total number of n-grams in the candidate translation.

The BLEU-4 score formula combines the brevity penalty and the modified n-gram precisions for n=1,2,3,4. If any of the modified n-gram precisions are zero, the BLEU-4 score is set to zero, indicating a poor translation quality. Understanding the components of the BLEU-4 score and how they interact is essential for implementing an accurate calculation.

Algorithm/Approach

The general approach to solving this problem involves the following steps:

Tokenize the reference and candidate translations into individual words or n-grams
Calculate the modified n-gram precisions for n=1,2,3,4
Compute the brevity penalty based on the length of the candidate and reference translations
Combine the brevity penalty and modified n-gram precisions to calculate the BLEU-4 score

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