@InProceedings{peng-EtAl:2018:N18-1,
  author    = {Peng, Hao  and  Thomson, Sam  and  Swayamdipta, Swabha  and  Smith, Noah A.},
  title     = {Learning Joint Semantic Parsers from Disjoint Data},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {1492--1502},
  abstract  = {We present a new approach to learning a semantic parser from multiple datasets, even when the target semantic formalisms are drastically different and the underlying corpora do not overlap. We handle such ``disjoint'' data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.},
  url       = {http://www.aclweb.org/anthology/N18-1135}
}