Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
  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 and Miguel Ballesteros and Chris Dyer and Noah A.   Smith
cs.CL cs.AI