Interpretable Neural Predictions with Differentiable Binary Variables
Table 5: SST hyperparameters.
['Optimizer', 'Adam']
For sentiment classification we make use of the PyTorch bidirectional LSTM module for encoding sentences, for both the rationale extractor and the classifier. The BiLSTM final states are concatenated, after which a linear layer followed by a softmax produces the prediction. We apply dropout to the embeddings and to the input of the output layer.