Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

Code

• github.com/allenai/pnp/tree/wikitables2/experiments/wikitables
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Abstract

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-the-art 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.

Tasks

Reproductions