BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering
Table 2: Ablation test results of BAG model on the unmasked validation set of the WIKIHOP dataset.
['[BOLD] Models', '[BOLD] Unmasked']
Once we remove the bi-directional attention and put the concatenation of nodes and queries directly into the output layer, it shows significant performance drop with more than 3%, proving the necessity of attention for reasoning in multi-hop QA. If we use linear-transformation-based single attention a=hnWafq given in Luong et al. instead of our bi-directional attention, the accuracy drops with 2%, which means attention bi-directionality also contributes to the performance improvement. The similar condition will appear if we remove GCN, but use raw nodes as input for the attention layer.