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Old Age
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
June 29, 2016 ยท Declared Dead ยท ๐ Conference on Computational Natural Language Learning
"No code URL or promise found in abstract"
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Authors
Swabha Swayamdipta, Miguel Ballesteros, Chris Dyer, Noah A. Smith
arXiv ID
1606.08954
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
63
Venue
Conference on Computational Natural Language Learning
Last Checked
4 months ago
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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