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 also has a large disadvantage in that the loss function expressed in Equation 17is not convex, and most MT evaluation measures used in the calculation of the error function error(•) are not continuously differentiable.