Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

  title={Neural Semantic Parsing with Type Constraints for Semi-Structured Tables},
  author={Jayant Krishnamurthy and Pradeep Dasigi and Matt Gardner},
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… CONTINUE READING
Highly Cited
This paper has 63 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.

63 Citations

Citations per Year
Semantic Scholar estimates that this publication has 63 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 37 references

Le , Martı́n Abadi , Andrew McCallum , and Dario Amodei . 2017 . Learning a natural language interface with neural programmer

  • Yoav Goldberg, Austin Matthews, +4 authors Trevor Cohn
  • DyNet : The dynamic neural network toolkit
  • 2017