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Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

06/29/2016

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by Swabha Swayamdipta, et al.

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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.

Swabha Swayamdipta
19 publications
Miguel Ballesteros
44 publications
Chris Dyer
79 publications
Noah A. Smith
127 publications

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