BLEU Score
BLEU (Bilingual Evaluation Understudy) Score is a metric used to evaluate the quality of machine-generated text, particularly in machine translation, by comparing it to one or more human-written reference translations. It calculates a score based on the precision of n-gram matches between the candidate and reference texts, with a brevity penalty to penalize overly short outputs. It is widely used as an automated, objective measure in natural language processing (NLP) research and development.
Developers should learn and use BLEU Score when working on machine translation systems, text generation models, or any NLP task requiring automated evaluation of output quality against references. It is essential for benchmarking models during training and development, as it provides a quick, reproducible metric to compare different algorithms or iterations. However, it should be complemented with human evaluation, as it has limitations in capturing semantic meaning and fluency.