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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.
The METEOR metric is designed to address some of the deficiencies inherent in the BLEU metric. The metric is based on the weighted harmonic mean of unigram precision and unigram recall. The metric was designed after research by Lavie (2004) into the significance of recall in evaluation metrics.
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]
Since IBM proposed and realized the system of BLEU [1] as the automatic metric for Machine Translation (MT) evaluation, [2] many other methods have been proposed to revise or improve it, such as TER, METEOR, [3] etc. However, there exist some problems in the traditional automatic evaluation metrics. Some metrics perform well on certain ...
METEOR (Metric for Evaluation of Translation with Explicit ORdering) is a metric for the evaluation of machine translation output. The metric is based on the harmonic mean of unigram precision and recall , with recall weighted higher than precision.
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written ...
However, paraphrases often have several lexically different but equally valid solutions, hurting BLEU and other similar evaluation metrics. [21] Metrics specifically designed to evaluate paraphrase generation include paraphrase in n-gram change (PINC) [21] and paraphrase evaluation metric (PEM) [22] along with the aforementioned ParaMetric ...
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 ...