Generative Incremental Dependency Parsing with Neural Networks
By Jan Buys, Phil Blunsom. 2015
19 citations recorded by the source corpus.
We propose a neural network model for scalable generative transition-based dependency parsing. A probability distribution over both sentences and transition sequences is parameterised by a feedforward neural network. The model surpasses the accuracy and speed of previous generative dependency parsers, reaching 91.1% UAS. Perplexity results show a strong improvement over n-gram language models, opening the way to the efficient integration of syntax into neural models for language generation.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Noah A. Smith
- Graham Neubig
- Yejin Choi
- Luke S. Zettlemoyer
- Yoav Goldberg
- Chris Dyer
- Alexander M. Rush
In Natural Language Processing
- Noah A. Smith
- Graham Neubig
- Mohit Bansal
- Chris Dyer
- Yoav Goldberg
- Yejin Choi
- Alexander M. Rush
- Luke S. Zettlemoyer
Citations in the corpus
- Recurrent Neural Network Grammars (2016, background): “While several transition-based neural models of syntactic generation exist (Henderson, 2003, 2004; Emami and Jelinek, 2005; Titov and Henderson, 2007; Buys and Blunsom, 2015b), these have relied on structure building operations based on parsing actions in shift-reduce and leftcorner parsers which op”
- A Fast Unified Model for Parsing and Sentence Understanding (2016, background): “There is a fairly long history of work on building neural network-based parsers that use the core operations and data structures from transition-based parsing, of which shift-reduce parsing is a variant (Henderson, 2004; Emami and Jelinek, 2005; Titov and Henderson, 2010; Chen and Manning, 2014; Buy”
- Unsupervised Recurrent Neural Network Grammars (2019, background): “Parser (Buys and Blunsom, 2015) 138.6 RNNLM (Buys and Blunsom, 2018) 100.7 Sup.”
- Improving Neural Parsing by Disentangling Model Combination and Reranking Effects (2017, methodology): “decoding procedures developed for other generative models (Henderson, 2003; Titov and Henderson, 2010; Buys and Blunsom, 2015) and can be viewed as a simplified version of the procedure used in the generative top-down parsers of Roark (2001) and Charniak (2010).”