Now, unlike CBOW which takes the sum of the context embedding of words in C wi to predict w i , we apply directed Graph Convolution Network (as defined in Section 3) on G s with context embeddings of words in s as input features.
for each word w i in s, we obtain a representation h k+1 i after k-layers of GCN using Equation 1 which we reproduce below for ease of readability (with one exception as described below).