On the other hand, EM-BED assigns each edge label to an embedding vector, but complicates the gated operations by changing the U s to be 3D tensors.
as the input to our DaG LSTM, we first calculate the representation for each edge (i, j, l) by: where W 1 and b 1 are model parameters, e i is the embedding of the source word indexed by i, and e l is the embedding of the edge label l. The baseline LSTM model learns DaG representations sequentially, following word orders.