@inproceedings{c4c3874c90604edf9383f436b2d5d53c,
title = "Learning joint semantic parsers from disjoint data",
abstract = "We present a new approach to learning semantic parsers 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. Our code is open-source and available at https://github.com/ Noahs-ARK/NeurboParser.",
author = "Hao Peng and Sam Thomson and Swabha Swayamdipta and Smith, {Noah A.}",
note = "Funding Information: We thank Kenton Lee, Luheng He, and Rowan Zellers for their helpful comments, and the anonymous reviewers for their valuable feedback. This work was supported in part by NSF grant IIS-1562364. Publisher Copyright: {\textcopyright} 2018 The Association for Computational Linguistics.; 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 01-06-2018 Through 06-06-2018",
year = "2018",
language = "English (US)",
series = "NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1492--1502",
booktitle = "Long Papers",
address = "United States",
}