Discovering Differential Features: Adversarial Learning for Information Credibility Evaluation
Table 5: The performance evaluation of different parts of the model
['LIAR', 'basic', '[BOLD] A 0.241', '[BOLD] P 0.254', '[BOLD] R 0.227', '[BOLD] F1 0.240']
We use the above models to test the validity of different components of our model. It indicates that the additional components in the basic method are helpful for credibility evaluation. Especially, the model with Diff outperforms obviously the model without Diff, which demonstrates the effectiveness of negative KL-divergence strategy in our model.