KagNet: Knowledge-Aware Graph Networksfor Commonsense Reasoning
Table 4: Ablation study on the KagNet framework.
['[BOLD] Model', 'IHdev.(%)', 'IHtest.(%)']
Ablation study on model components. We find that replacing our GCN-LSTM-HPA architecture with traditional relational GCNs, which uses separate weight matrices for different relation types, results in worse performance, due to its over-parameterization. The attention mechanisms matters almost equally in two levels, and pruning also effectively filters noisy paths.