When predicting the transition action, the feature function φ(c) that summarizes the current configuration c = (σ, β, T ) is simply the concatenated BiLSTM vectors of the top three elements in the stack and the first element in the buffer: MLP Scoring Function The score of transition action y under the current configuration c is determined by a multi-layer perceptron with one hidden layer: where Hinge Loss Function The training objective is to raise the scores of correct transitions above scores of incorrect ones.
at each step, we use a hinge loss defined as: where Y is the set of possible transitions and Y + is the set of correct transitions at the current step.