MIRA learns and outputs a linear classification score, s(x, y; w) = w • f (x, y), where the feature representation f can contain arbitrary features of the input, including the correspondence features described earlier.
mIRA aims to learn weights so that the score of correct output, y t , for input x t is separated from the highest scoring incorrect outputs 2 , with a margin proportional to their Hamming losses.