Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
Jayant Krishnamurthy, Pradeep Dasigi, Matt Gardner · 2017
We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables.Our parser is an encoder-decoder neural network with two key technical innovations:(1) a grammar for the decoder that only generates well-typed logical forms; and(2) an entity embedding and linking module that identifies entity mentions while generalizing across tables.We also introduce a novel method for training our neural model with question-answer supervision.On the WIKITABLEQUESTIONS data set, our parser achieves a state-of-theart accuracy of 43.3% for a single model and 45.9% for a 5-model ensemble, improving on the best prior score of 38.7% set by a 15-model ensemble.These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.