Question Answering by Reasoning Across Documents with Graph Convolutional Networks
Table 3: Ablation study on WikiHop validation set. The full model is our Entity-GCN with all of its components and other rows indicate models trained without a component of interest. We also report baselines using GloVe instead of ELMo with and without R-GCN. For the full model we report mean±1std over 5 runs.
['[BOLD] Model', 'unmasked', 'masked']
We perform two groups of ablation, one on the embedding layer, to study the effect of ELMo, and one on the edges, to study how different relations affect the overall model performance. and even above the Coref-GRU of Dhingra et al. That is, the R-GCN component pushes the model to perform above Coref-GRU still without accessing context, but rather by updating mention representations based on their relation to other ones. These results highlight the impact of our R-GCN component.