Class model parameters are updated in a simple additive fashion: Parameters are altered according to the gradient with respect to MSELoss i (λ λ λ).
in our implementation, we approximate w R as the w in GEN(s) with the fewest errors, so Score(λ λ λ, s, w R ) − Score(λ λ λ, s, w) ≤ 0, where the equality holds only when w R = w. the model parameters are updated when the sentence is wrongly segmented.