Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
Table 5: Hyperparameters for each model.
['model', 'LSTM size', 'dropout', 'batch size', 'learning rate']
For finding the best model configurations, we tuned the hyperparamters by training each model on the training data of all topics and evaluated their performance on all validation sets. In particular, we experimented with LSTM sizes of 32, 48, 64, 96, 128, 160, and 192; dropouts of 0.1, 0.3, 0.5, 0.7, and 0.9; batch sizes of 16, 32, 64, and 128; and learning rates of 1×10−2,5×10−3,1×10−3, and 1×10−4.