Learning Joint Semantic Parsers from Disjoint Data

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Authors Swabha Swayamdipta, Hao Peng, Noah A. Smith, Sam Thomson
Journal/Conference Name NAACL 2018 6
Paper Category
Paper 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.
Date of publication 2018
Code Programming Language TeX
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