Attention Guided Graph Convolutional Networks for Relation Extraction
Table 4: An ablation study for C-AGGCN model.
['[BOLD] Model', 'F1']
We examine the contributions of two main components, namely, densely connected layers and attention guided layers, using the best-performing C-AGGCN model on the TACRED dataset. We can observe that adding either attention guided layers or densely connected layers improves the performance of the model. This suggests that both layers can assist GCNs to learn better information aggregations, producing better representations for graphs, where the attention-guided layer seems to be playing a more significant role. We also notice that the feed-forward layer is effective in our model. Without the feed-forward layer, the result drops to an F1 score of 67.8.