@InProceedings{krishnamurthy-dasigi-gardner:2017:EMNLP2017,
  author    = {Krishnamurthy, Jayant  and  Dasigi, Pradeep  and  Gardner, Matt},
  title     = {Neural Semantic Parsing with Type Constraints for Semi-Structured Tables},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1516--1526},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1160}
}