Neural Semantic Parsing with Type Constraints for Semi-Structured Tables

@inproceedings{Krishnamurthy2017NeuralSP,
  title={Neural Semantic Parsing with Type Constraints for Semi-Structured Tables},
  author={Jayant Krishnamurthy and Pradeep Dasigi and Matt Gardner},
  booktitle={EMNLP},
  year={2017}
}
Highlight Information
We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. [...] Key Method 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…Expand Abstract

Figures, Tables, and Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 91 CITATIONS

It was the training data pruning too!

VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Grammar-based Neural Text-to-SQL Generation

VIEW 7 EXCERPTS
CITES BACKGROUND & METHODS

Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

VIEW 8 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Semantic Parsing with Syntax- and Table-Aware SQL Generation

VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Did the Model Understand the Question?

VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Execution-Guided Neural Program Decoding

VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2017
2020

CITATION STATISTICS

  • 11 Highly Influenced Citations

  • Averaged 29 Citations per year from 2017 through 2019

  • 32% Increase in citations per year in 2019 over 2018

References

Publications referenced by this paper.
SHOWING 1-10 OF 36 REFERENCES

Learning Dependency-Based Compositional Semantics

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

DyNet: The Dynamic Neural Network Toolkit

VIEW 2 EXCERPTS

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