Shift-Reduce Constituent Parsing with Neural Lookahead Features
Table 4: Performance of the constituent hierarchy predictor and the corresponding parser on the WSJ dev dataset. n-layer denotes a LSTM model with n hidden layers.
['[EMPTY]', '[ITALIC] s-type', '[ITALIC] e-type', 'parser']
The third column shows the development parsing accuracies when the labels are used for lookahead features. The accuracies of e-type prediction is relatively lower due to right-branching in the treebank, which makes e-type hierarchies longer than s-type hierarchies. In addition, a 3-layer LSTM does not give significantly improvements compared to 2-layer LSTM. For tradeoff between efficiency and accuracy, we choose the 2-layer LSTM as our constituent hierarchy predictor.