What is BLEU (Bilingual Evaluation Understudy)

BLEU (Bilingual Evaluation Understudy) is a metric used in Machine Learning to evaluate the performance of a machine translation system. It was developed by IBM in the early 2000s and has since become the standard for measuring the accuracy of machine translation.

BLEU stands for Bilingual Evaluation Understudy, which means that it is used to evaluate how well a machine translation system is performing in translating from one language to another. The metric is based on the idea of comparing the output of a machine translation system to a human translation of the same source text. The output of the machine translation system is compared to the human translation in terms of the words used, the order of the words, and the grammar used. The score is then calculated based on the number of matches between the two.

BLEU is a widely used metric because it is relatively simple and easy to understand. It is also relatively consistent and reliable, meaning that it gives a good indication of how well a machine translation system is performing.

BLEU is most often used to evaluate the performance of machine translation systems that are translating between two languages, such as English and Spanish. However, it can also be used to evaluate the performance of systems that are translating between multiple languages, such as English, French and German.

BLEU is an important metric in Machine Learning because it helps to identify the strengths and weaknesses of a machine translation system. This can then be used to improve the system and make it more accurate and reliable.