GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
Table 4: Sentence selection evaluation and average label accuracy of GEAR with different thresholds on dev set (%).
['[ITALIC] τ', '[BOLD] OFEVER', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1', '[BOLD] GEAR LA']
We find the model with threshold 0 achieves the highest recall and OFEVER score. When the threshold increases, the recall value and the OFEVER score drop gradually while the precision and F1 score increase. The results are consistent with our intuition. If we do not filter out evidence, more claims could be provided with the full evidence set. If we increase the value of the threshold, more pieces of noisy evidence are filtered out, which contributes to the increase of precision and F1.