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A substantial thread of recent work on latent tree learning has attempted to develop neural network models with parse-valued latent variables and train them on non-parsing tasks, in the hope of having them discover interpretable tree structure.
Two experiments on learning probabilistic dependency grammars from corpora
Eugene Charniak and Glen Carroll. 1992 · 1992
Earlier work this paper cites.
Long Short Term Memory
Sepp Hochreiter and Jürgen Schmidhuber. 1996 · 1993
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Building a large annotated corpus of english: The penn treebank
Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993 · 1993
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Learning task-dependent distributed representations by backpropagation through structure
Christoph Goller and Andreas Kuchler. 1996 · 1996
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A generative constituent-context model for improved grammar induction
Dan Klein and Christopher D. Manning. 2002 · 2002
Earlier work this paper cites.
Incremental parsing with the perceptron algorithm
Michael Collins and Brian Roark. 2004 · 2004
Earlier work this paper cites.
Natural language grammar induction with a generative constituent-context model
Dan Klein and Christopher D. Manning. 2005 · 2005
Cited alongside, same era.
Guiding unsupervised grammar induction using contrastive estimation
Noah A. Smith and Jason Eisner. 2005 · 2005
Cited alongside, same era.
An All-Subtrees Approach to Unsupervised Parsing
Rens Bod. 2006 · 2006
Cited alongside, same era.
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Richard Socher, Cliff Chiung-Yu Lin, Andrew Ng, and Chris Manning. 2011 · 2011
Cited alongside, same era.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Structured attention networks
Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. 2017 · 2017
Later among the works it cites.
Jointly learning sentence embeddings and syntax with unsupervised Tree-LSTMs
Jean Maillard, Stephen Clark, and Dani Yogatama. 2017 · 2017
Later among the works it cites.
Learning to Compose Words into Setences with Reinforcement Learning
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. 2017 · 2017
Later among the works it cites.
Learning to compose task-specific tree structures
Jihun Choi, Kang Min Yoo, and Sang-goo Lee. 2018 · 2018
Closest in time.
Neural language modeling by jointly learning syntax and lexicon
Yikang Shen, Zhouhan Lin, Chin wei Huang, and Aaron Courville. 2018 · 2018
Closest in time.
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Cited alongside, same era.
Do latent tree learning models identify meaningful structure in sentences?
Adina Williams, Andrew Drozdov, and Samuel R. Bowman. 2018a
Cited in the paper.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018b
Cited in the paper.