O Pontuação Bleu Métrica, often abbreviated as BLEU, is a popular métrica de avaliação used in the field of Processamento de Linguagem Natural (NLP) to assess the quality of text produced by tradução automática systems and other text generation models. Developed in the early 2000s, BLEU measures how closely the output of a model aligns with one or more reference texts, typically human-generated translations or summaries.
BLEU operates on the principle of comparing n-grams (contiguous sequences of n items) in the generated text with those in the reference texts. The basic formula for BLEU involves calculating the precision of n-grams, which is the ratio of the number of overlapping n-grams in the generated text to the total number of n-grams. BLEU also incorporates a brevity penalty to discourage short translations that might achieve high precision but fail to convey the full meaning of the source text.
A métrica retorna uma pontuação que varia de 0 a 1, onde uma pontuação de 1 indica uma correspondência perfeita com os textos de referência. No entanto, a pontuação BLEU possui algumas limitações; ela foca principalmente na precisão e pode ignorar diferenças contextuais ou semânticas importantes. Além disso, pode ser sensível ao comprimento do texto de saída, motivo pelo qual a penalidade de brevidade é incluída.
Despite its drawbacks, BLEU remains widely used because it provides a straightforward and quantitative way to evaluate and compare machine-generated text against human standards. It has been instrumental in benchmarking various NLP systems and continues to evolve with the advancement of tecnologias de IA.