BLEU is a widely used evaluation metric in natural language processing (NLP) for assessing the quality of machine-generated text, particularly in tasks like machine translation. It measures how closely the generated text matches one or more reference texts by analyzing word overlaps.
How It Works:
- N-Gram Matching: BLEU compares sequences of words (n-grams) in the generated text with those in the reference text. Commonly, unigrams (single words) to 4-grams (sequences of 4 words) are used.
- Precision: It calculates the proportion of n-grams in the generated text that appear in the reference text.
- Brevity Penalty: BLEU penalizes overly short translations to ensure outputs are not unfairly rewarded for being concise.
- Final Score: A weighted average of n-gram precision scores is calculated, resulting in a single value between 0 and 1, where higher scores indicate better alignment with the reference.
Why It Matters:
BLEU is popular because it provides an automated, reproducible way to evaluate machine translation quality. However, it has limitations, such as focusing on exact matches and struggling with capturing nuances like fluency or meaning. For this reason, BLEU is often complemented by other metrics like ROUGE or BERTScore in modern NLP evaluations.
Despite its constraints, BLEU remains a cornerstone metric, particularly for comparing models and tracking progress over time.
