@InProceedings{saparov-saraswat-mitchell:2017:CoNLL,
  author    = {Saparov, Abulhair  and  Saraswat, Vijay  and  Mitchell, Tom},
  title     = {A Probabilistic Generative Grammar for Semantic Parsing},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {248--259},
  abstract  = {We present a generative model of natural language sentences and demonstrate its
	application to semantic parsing. In the generative process, a logical form
	sampled from a prior, and conditioned on this logical form, a grammar
	probabilistically generates the output sentence. Grammar induction using MCMC
	is applied to learn the grammar given a set of labeled sentences with
	corresponding logical forms. We develop a semantic parser that finds the
	logical form with the highest posterior probability exactly. We obtain strong
	results on the GeoQuery dataset and achieve state-of-the-art F1 on Jobs.},
  url       = {http://aclweb.org/anthology/K17-1026}
}