Later, Kulesza and Shieber (2004) noted that if a classifier provides a confidence score for its output, that value can be interpreted as a quantitative estimate of the input instance's translation quality.
they trained an SVM classifier that makes its decisions based on a set of input features computed from the sentence to be evaluated; the distance between input feature vector and the separating hyperplane then serves as the evaluation score.