GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
Table 5: Label accuracy on the difficult dev set with different ERNet layers and evidence aggregators (%).
['[BOLD] ERNet Layers', '[BOLD] Aggregator  [BOLD] Attention', '[BOLD] Aggregator  [BOLD] Max', '[BOLD] Aggregator  [BOLD] Mean']
We find our models with ERNet perform better than models without ERNet and the minimal improvement between them is 1.27%. We can also discover from the table that models with 2 ERNet layers achieve the best results, which indicates that claims from the difficult subset require multi-step evidence propagation. This result demonstrates the ability of our framework to deal with claims which need multiple evidence.