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Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

2016-06-29CONLL 2016Code Available0· sign in to hype

Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, Noah A. Smith

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Abstract

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.

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Semantic Parsing

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