@inproceedings{peng-etal-2018-learning,
    title = "Learning Joint Semantic Parsers from Disjoint Data",
    author = "Peng, Hao  and
      Thomson, Sam  and
      Swayamdipta, Swabha  and
      Smith, Noah A.",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/N18-1135/",
    doi = "10.18653/v1/N18-1135",
    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."
}