The original GCNs compute a graph convolution vector for w i at (k+1)th layer by: where and b k L(w i ,v) are parameters of the dependency label L(w i , v) in the kth layer.
retaining parameters for every dependency label is space-consuming and compute-intense (there are approximately 50 labels), in our model, we limit L(w i , v) to have only three types of labels 1) an original edge, 2) a self loop edge, and 3) an added inverse edge, as suggested in (Nguyen and Grishman, 2018).