Fully Automated Fact Checking Using External Sources
Table 4: Results on the cQA answer fact-checking problem.
['[BOLD] Model', '[BOLD] False Claims  [BOLD] P', '[BOLD] False Claims  [BOLD] R', '[BOLD] False Claims  [BOLD] F1', '[BOLD] True Claims  [BOLD] P', '[BOLD] True Claims  [BOLD] R', '[BOLD] True Claims  [BOLD] F1', '[BOLD] Overall  [BOLD] AvgR', '[BOLD] Overall  [BOLD] AvgF1', '[BOLD] Overall  [BOLD] Acc']
Once again, our models outperformed all baselines by a margin. This time, the predictions of all models are balanced between the two classes, which is probably due to the dataset being well balanced in general. The SVM model performs better than the NN by itself. This is due to the fact that the cQA dataset is significantly smaller than the rumor detection one. Thus, the neural network could not be trained effectively by itself. Nevertheless, the task-specific representations were useful and combining them with the SVM model yielded consistent improvements on all the measures once again.