A Unified Deep Learning Architecture for Abuse Detection
Table 2: Final results of the baselines and our experiments, for each one of the datasets.
['[EMPTY]', 'AUC', 'Acc.', 'Prec.', 'Rec.', 'F1']
Training methodology Here, we test the training methods discussed earlier to choose the best method to compare with the state-of-art. Firstly, we observe that training with the whole network at once (naive training) results to suboptimal performance (e.g., AUC of 0.94 in the cyberbullying dataset). The reason is that allowing the gradient descent to update both paths simultaneously might result in unwanted interactions between the two. For example, one path might converge faster than the other, dominating in the decisions. In fact, when we examine the standalone classifiers, we observe that the text classifier requires 25-40 epochs to converge whereas the metadata classifier only requires 7-12. By training the whole network together, the metadata side can start overfitting.