Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims
Table 5. CLEF-2019 Test Dataset Classification and Ranking Task Results
['[BOLD] Model', '[BOLD] Training Dataset', '[BOLD] mAP', '[BOLD] P@10', '[BOLD] P@20', '[BOLD] P@50', '[BOLD] P  [BOLD] NCS', '[BOLD] P  [BOLD] CFS', '[BOLD] R  [BOLD] NCS', '[BOLD] R  [BOLD] CFS', '[BOLD] F1  [BOLD] NCS', '[BOLD] F1  [BOLD] CFS', '[BOLD] nDCG']
For this reason we also cannot provide the P, R, F1, and nDCG for these teams. We tested models trained on both the CBD and C2019 training set and used the C2019 testing set to evaluate them. The models trained on CBD and tested on the CLEF test set didn’t perform as well; this was expected, given that our methodology of dataset labelling differs significantly from CLEF’s. Despite this, when trained on C2019, CB-BBA held its own when compared to the Copenhagen model which was the winner of that competition only falling short in the mAP metric by 0.0035.