Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
Swabha Swayamdipta,Miguel Ballesteros,Chris Dyer,Noah A. Smith
2016 · DOI: 10.18653/v1/K16-1019
Conference on Computational Natural Language Learning · 63 Citations
TLDR
This work presents a transition-based parser that jointly produces syntactic and semantic dependencies and obtains the best published parsing performance among models that jointly learn syntax and semantics.
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.