Type-Driven Incremental Semantic Parsing with Polymorphism
Table 1: Performances (precision, recall, and F1) of various parsing algorithms on GeoQuery, Jobs, and Atis datasets. ⋆: λ-WASP for GeoQuery is trained on 792 examples.
['System', 'GeoQuery P', 'GeoQuery R', 'GeoQuery F1', 'Jobs P', 'Jobs R', 'Jobs F1', 'Atis P', 'Atis R', 'Atis F1']
Given that all other methods use CKY-style parsing, our method is well balanced between accuracy and speed. In addition, to unveil the helpfulness of our type system, we train a parser with only simple types. In this setting, the predicates only have primitive types of location lo, integer i, and boolean t, while the constants still keep their types. It still has the type system, but it is weaker than the polymorphic one. Its accuracy is lower than the standard one, mostly caused by that the type system can not help pruning the wrong applications like (\bf{population}:au→i %mississippi:rv). This is actually because our method parses a lot more questions in the dataset, as the column of the percentage of successfully parsed sentences suggests. Atis dataset contains more than 5,000 examples and is a lot larger than GeoQuery and Jobs. Our method achieves comparable performance on this dataset. Due to space constraints, we do not show its type hierarchy here.