Understanding Neural Networks through Representation Erasure
Table 3: Importance score (computed using Eq.1) for a few sentiment indicators assigned by different models.
['word', 'Bi-LSTMs', 'Uni-LSTMs', 'RNN']
The ranking score is computed by averaging the log-likelihood difference resulting from erasing that word across all test examples containing the word. We can see that the Bi-LSTM is more sensitive to the deletion of these sentiment indicators than the Uni-LSTM, which is in turn more sensitive than the RNN. This is presumably due to the gate structures in LSTMs that control information flow, making these architectures better at focusing on words that indicate sentiment.