Learning an Executable Neural Semantic Parser
Table 9: Distantly supervised experimental results on the Spades dataset.
['Models', 'F1']
Previous work on this dataset has used a semantic parsing framework where natural language is converted to an intermediate syntactic representation and then grounded to Freebase. Specifically, Bisk et al. As can be seen, tnsp outperforms all CCG variants (from unsupervised to fully supervised) without having access to any manually annotated derivations or lexicons. Again, we observe that a top-down tnsp system with soft attention performs best and is superior to the sequence-to-sequence baseline.