The key idea of SGTB is that, instead of defining a parametric model and optimizing its parameters, we can directly optimize the factor scoring function F (•) iteratively by performing gradient descent in function space.
suppose F (•) = F m−1 (•) in the m-th iteration, we will update F (•) as follows: where is the functional gradient, η m is the learning rate, and 1 [•] represents an indicator function, which returns 1 if the predicted sequence matches the gold one, and 0 otherwise.