Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

06/29/2016

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by   Swabha Swayamdipta, et al.

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Universitat Pompeu Fabra

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Carnegie Mellon University

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University of Washington

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We present a transition-based parser that jointly produces syntactic and semantic dependencies. It learns a representation of the entire algorithm state, using stack long short-term memories. Our greedy inference algorithm has linear time, including feature extraction. On the CoNLL 2008--9 English shared tasks, we obtain the best published parsing performance among models that jointly learn syntax and semantics.