An Effective Label Noise Model for DNN Text Classification
Table 3: SVM Classification
['Data(N%)', 'TRB WoNM', 'TRB Noisy', 'TRB True', 'TRPr NMwRegu01', 'TRPr Noisy', 'TRPr True']
In order to further understand the noise model, we first train the base model and the proposed model on noisy labels. Afterward, we collect the last fully-connected layer’s activations for all the training samples and treat them as the learned feature representation of the input sentence. We get two different sets of feature representations, one corresponding to the base model (TRB), and the other corresponding to the proposed model (TRPr). Given these learned feature representations – the artificially injected noisy labels and the true labels of the training data – we learn two different SVMs for each model, with and without noise. For the base model, for both SVMs, we use TRB representation as inputs and train the first SVM with the true labels as targets and the second SVM with the unreliable labels as targets. Similarly, we train two SVMs for the proposed model. We run these experiments for different datasets with different label noise. This suggests that the base model is just fitting the noisy labels. On the other hand, when we train an SVM on the TRPr representations with true labels as targets, the SVM achieves the proposed model performance. This means that the proposed approach helps the base model to learn better feature representations even with the noisy targets, which suggest that this noise model is learning a label denoising operator.