Given two translation hypotheses t 1 and t 2 (and a reference translation r), we want to tell which of the two is better.
2 we have a binary classification task, which is modeled by the class variable y, defined as follows: y = 1 if t 1 is better than t 2 given r 0 if t 1 is worse than t 2 given r (1) We model this task using a feed-forward neural network (NN) of the form: which is a Bernoulli distribution of y with parameter σ = f (t 1 , t 2 , r), defined as follows: where sig is the sigmoid function, φ(x) defines the transformations of the input x through the hidden layer, w v are the weights from the hidden layer to the output layer, and b v is a bias term.