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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.
This differs from the BLEU metric in that BLEU seeks correlation at the corpus level. Example alignment (a). Results have been presented which give correlation of up to 0.964 with human judgement at the corpus level, compared to BLEU's achievement of 0.817 on the same data set. At the sentence level, the maximum correlation with human judgement ...
The quality of a translation is inherently subjective, there is no objective or quantifiable "good." Therefore, any metric must assign quality scores so they correlate with the human judgment of quality. That is, a metric should score highly translations that humans score highly, and give low scores to those humans give low scores.
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, [1] is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing.
Read the full story. Associated Press. Pacers overcome slow start with 50-point 2nd quarter and pull away from Grizzlies 127-113; Yahoo Sports. ... Jamal Murray scores 34, Nuggets hold off Hornets ...
in which refers to the quantity being scaled (i.e. , , , number of training steps, number of inference steps, or model input size) and refers to the downstream (or upstream) performance evaluation metric of interest (e.g. prediction error, cross entropy, calibration error, AUROC, BLEU score percentage, F1 score, reward, Elo rating, solve rate ...
SOURCE: Integrated Postsecondary Education Data System, University of Illinois at Urbana-Champaign (2014, 2013, 2012, 2011, 2010).Read our methodology here.. HuffPost and The Chronicle examined 201 public D-I schools from 2010-2014.
It is based on the BLEU metric, but with some alterations. Where BLEU simply calculates n-gram precision adding equal weight to each one, NIST also calculates how informative a particular n-gram is. That is to say when a correct n-gram is found, the rarer that n-gram is, the more weight it will be given. [1]