Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
Table 6: Hyperparameters for training the text classification models.
['[BOLD] Model', '[BOLD] Word Embedding', '[BOLD] Hidden Size', '[BOLD] Layer', '[BOLD] Kernel Size']
All models are trained on cross-entropy loss, and their hyper-parameters are tuned on the validation sets. We used the Adam optimizer (with a learning rate 0.5\times 10^{-3}), and set the dropout rate on word embedding to 0.3, the weight decay to 1\times 10^{-4}, the batch size to 128, the maximum number of epochs to 20, and the gradient clip to (-1,1).