DBpedia – Linked Data Fragments

Matches in DBpedia 2015-10 for { ?s ?p "BLEU (Bilingual Evaluation Understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to achieve a high correlation with human judgements of quality, and remains one of the most popular automated and inexpensive metrics.Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations. Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness are not taken into account.BLEU is designed to approximate human judgement at a corpus level, and performs badly if used to evaluate the quality of individual sentences.BLEU’s output is always a number between 0 and 1. This value indicates how similar the candidate and reference texts are, with values closer to 1 representing more similar texts. However, few human translations will attain a score of 1. The candidate texts must be identical to a reference translation. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional reference translations will increase the BLEU score."@en }

• BLEU abstract "BLEU (Bilingual Evaluation Understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to achieve a high correlation with human judgements of quality, and remains one of the most popular automated and inexpensive metrics.Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations. Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness are not taken into account.BLEU is designed to approximate human judgement at a corpus level, and performs badly if used to evaluate the quality of individual sentences.BLEU’s output is always a number between 0 and 1. This value indicates how similar the candidate and reference texts are, with values closer to 1 representing more similar texts. However, few human translations will attain a score of 1. The candidate texts must be identical to a reference translation. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional reference translations will increase the BLEU score.".