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.
REF present a transition-based stack LSTM model that predicts syntax and semantics jointly, as a remedy to the reliance on pipelined models.
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
ArXiv
ArXiv
Computer Science