Converting this to a loss function that is dependent on the model parameters, we obtain the following loss expressing the error over the 1-best results obtained by decoding in Equation 1: Error has the advantage of being simple, easy to explain, and directly related to translation performance, and these features make it perhaps the most commonly used loss in current machine translation systems.
it is also possible to perform leaving-one-out training, where counts of rules extracted from a particular sentence are subtracted from the model before translating the sentence (Wuebker, Mauser, and Ney 2010).