Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

Swabha Swayamdipta1, Miguel Ballesteros2, Chris Dyer3, Noah A. Smith4
1Carnegie Mellon University, 2Pompeu Fabra University, 3Google DeepMind, 4University of Washington


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.