Zero-Shot Learning with Common Sense Knowledge Graphs
Table 9: Output dimensions of the graph neural networks in our experiments
['[ITALIC] Task', '[ITALIC] layer-1', '[ITALIC] layer-2']
GCN, DGP, GCNZ, and SGN are linear aggregators and learn only one weight matrix in each of the layers. GAT learns a weight matrix for the attention where Wa∈R2d(l)×1 and uses LeakyReLU activation in the attention. LeakyReLU has a negative slope of 0.2. RGCN learns B bases weight vectors in the baseline. We found that B=1 performs the best for fine-grained entity typing and object classification. For intent classification, we use 10 bases, i.e., B=10. In intent classification and fine-grained entity typing, the non-linear activation function after the graph neural network layer is ReLU and in object classification the activation function is LeakyReLU with a negative slope of 0.2.