Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
Table 5: Comparison and ablation study: the columns report averaged prediction accuracy, training time, and prediction time (milliseconds per example) on the three train-dev splits.
['[EMPTY]', 'Acc.', 'Time (ms/ex) Train', 'Time (ms/ex) Pred']
The time for constructing the macro grammar is included as part of the training time. However, training with the macro grammar is substantially more efficient than training with only the base grammar— it achieves 11x speedup for training and 16x speedup for test time prediction. We run two ablations of our algorithm to evaluate the utility of holistic triggering and macro decomposition. This is because holistic triggering effectively prunes irrelevant macro rules, while macro decomposition is important for efficient beam search and featurization.