@InProceedings{zhang-pasupat-liang:2017:EMNLP2017,
  author    = {Zhang, Yuchen  and  Pasupat, Panupong  and  Liang, Percy},
  title     = {Macro Grammars and Holistic Triggering for Efficient Semantic Parsing},
  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     = {1214--1223},
  abstract  = {To learn a semantic parser from denotations, a learning algorithm must search
	over a combinatorially large space of logical forms for ones consistent with
	the annotated denotations. We propose a new online learning algorithm that
	searches faster as training progresses. The two key ideas are using macro
	grammars to cache the abstract patterns of useful logical forms found thus far,
	and holistic triggering to efficiently retrieve the most relevant patterns
	based on sentence similarity. On the WikiTableQuestions dataset, we first
	expand the search space of an existing model to improve the state-of-the-art
	accuracy from 38.7% to 42.7%, and then use macro grammars and holistic
	triggering to achieve an 11x speedup and an accuracy of 43.7%.},
  url       = {https://www.aclweb.org/anthology/D17-1125}
}