Shift-Reduce Constituent Parsing with Neural Lookahead Features
Table 5: Performance of the constituent hierarchy predictor and the corresponding parser on the WSJ dev dataset. all denotes the proposed model without ablation.
['[EMPTY]', '[ITALIC] s-type', '[ITALIC] e-type', 'parser']
We find that the original architecture achieves the highest performance on constituent hierarchy prediction, compared to the two baselines. The baseline only without the character embeddings has relatively small influence on constituent hierarchy prediction. On the other hand, the baseline only without the input word windows has relatively smaller influence on constituent hierarchy prediction. Nevertheless, both of these two ablation architectures lead to much lower parsing accuracies. The baseline removing both the character embeddings and the input word windows has relatively low accuracies.