@InProceedings{herzig-berant:2017:Short,
  author    = {Herzig, Jonathan  and  Berant, Jonathan},
  title     = {Neural Semantic Parsing over Multiple Knowledge-bases},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {623--628},
  abstract  = {A fundamental challenge in developing semantic parsers is the paucity of strong
	supervision in the form of language utterances annotated with logical form. In
	this paper, we propose to exploit structural regularities in language in
	different domains, and train semantic parsers over multiple knowledge-bases
	(KBs), while sharing information across datasets. We find that we can
	substantially improve parsing accuracy by training a single
	sequence-to-sequence model over multiple KBs, when providing an encoding of the
	domain at decoding time. Our model achieves state-of-the-art performance on the
	Overnight dataset (containing eight domains), improves performance over a
	single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the
	number of model parameters.},
  url       = {http://aclweb.org/anthology/P17-2098}
}