What is BLEU Score?
The BLEU Score (Bilingual Evaluation Understudy) is a widely-used metric for assessing the quality of machine-generated translations. Developed in 2002, it provides a quantitative measure of how closely a generated text matches one or more human-produced reference texts. The score ranges from 0 to 1, where 1 indicates a perfect match with the reference texts.
How is BLEU Score calculated?
To calculate the BLEU Score, the method looks at the overlap of n-grams (continuous sequences of n items from the text) between the generated text and the reference texts. The score is computed based on the precision of these n-grams, which measures the proportion of n-grams in the generated text that also appear in the reference texts. To ensure a balanced evaluation, BLEU incorporates a penalty for shorter translations, as shorter outputs may achieve high precision without capturing the full context.
Why is BLEU Score important?
BLEU Score has become a standard evaluation method in the field of Natural Language Processing (NLP) and is particularly valuable in machine translation tasks. By providing a consistent and objective way to assess translation quality, it helps researchers and developers compare different translation models and track improvements over time. However, it’s important to note that while BLEU can provide insights into translation quality, it does not fully encompass all aspects of language fluency and meaning, making it essential to use in conjunction with human evaluations.