Fetching the paper…
Reading the bibliography…
Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses).
Three models for the description of language
Noam Chomsky · 1956
Earlier work this paper cites.
Aspects of the Theory of Syntax
Noam Chomsky · 1965
Earlier work this paper cites.
Neural sequence chunkers
Jürgen Schmidhuber · 1991
Earlier work this paper cites.
Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini · 1993
Earlier work this paper cites.
Bayesian grammar induction for language modeling
Stanley F Chen · 1995
Earlier work this paper cites.
Hierarchical recurrent neural networks for long-term dependencies
Salah El Hihi and Yoshua Bengio · 1996
Earlier work this paper cites.
Learning long-term dependencies is not as difficult with narx recurrent neural networks
Tsungnan Lin, Bill G Horne, Peter Tino, and C Lee Giles · 1998
Earlier work this paper cites.
Structured language modeling
Ciprian Chelba and Frederick Jelinek · 2000
Earlier work this paper cites.
Immediate-head parsing for language models
Eugene Charniak · 2001
Earlier work this paper cites.
Lstm recurrent networks learn simple context-free and context-sensitive languages
Felix A Gers and E Schmidhuber · 2001
Earlier work this paper cites.
Probabilistic top-down parsing and language modeling
Brian Roark · 2001
Earlier work this paper cites.
A generative constituent-context model for improved grammar induction
Dan Klein and Christopher D Manning · 2002
Earlier work this paper cites.
Accurate unlexicalized parsing
Dan Klein and Christopher D Manning · 2003
Earlier work this paper cites.
Natural language grammar induction with a generative constituent-context model
Dan Klein and Christopher D Manning · 2005
Earlier work this paper cites.
An all-subtrees approach to unsupervised parsing
Rens Bod · 2006
Earlier work this paper cites.
Learning deep architectures for ai
Yoshua Bengio et al · 2009
Earlier work this paper cites.
Learning continuous phrase representations and syntactic parsing with recursive neural networks
Richard Socher, Christopher D Manning, and Andrew Y Ng · 2010
Earlier work this paper cites.
Unsupervised structure prediction with non-parallel multilingual guidance
Shay B Cohen, Dipanjan Das, and Noah A Smith · 2011
Earlier work this paper cites.
Statistical language models based on neural networks
Tomáš Mikolov · 2012
Earlier work this paper cites.
An introduction to syntactic analysis and theory, 2013
Hilda Koopman, Dominique Sportiche, and Edward Stabler · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts · 2013
Earlier work this paper cites.
Recursive neural networks can learn logical semantics
Samuel R Bowman, Christopher Potts, and Christopher D Manning · 2014
Earlier work this paper cites.
Jan Koutnik, Klaus Greff, Faustino Gomez, and Juergen Schmidhuber · 2014
Earlier work this paper cites.
Learning ordered representations with nested dropout
Oren Rippel, Michael Gelbart, and Ryan Adams · 2014
Cited alongside, same era.
Morphological Structure, Lexical Representation and Lexical Access (RLE Linguistics C: Applied Linguistics): A Special Issue of Language and Cognitive Processes
Dominiek Sandra and Marcus Taft · 2014
Cited alongside, same era.
Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
Cited alongside, same era.
Tree-structured composition in neural networks without tree-structured architectures
Samuel R Bowman, Christopher D Manning, and Christopher Potts · 2015
Cited alongside, same era.
The neural representation of sequences: from transition probabilities to algebraic patterns and linguistic trees
Stanislas Dehaene, Florent Meyniel, Catherine Wacongne, Liping Wang, and Christophe Pallier · 2015
Learning to compose words into sentences with reinforcement learning
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling · 2016
Later among the works it cites.
Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, and Jürgen Schmidhuber · 2016
Later among the works it cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Later among the works it cites.
On the state of the art of evaluation in neural language models
Gábor Melis, Chris Dyer, and Phil Blunsom · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning to transduce with unbounded memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, and Phil Blunsom · 2015
Cited alongside, same era.
Inferring algorithmic patterns with stack-augmented recurrent nets
Armand Joulin and Tomas Mikolov · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Cited alongside, same era.
Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
Cited alongside, same era.
Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
Cited alongside, same era.
Top-down tree long short-term memory networks
Xingxing Zhang, Liang Lu, and Mirella Lapata · 2015
Cited alongside, same era.
Stephen Merity, Nitish Shirish Keskar, and Richard Socher · 2017
Later among the works it cites.
Using the output embedding to improve language models
Ofir Press and Lior Wolf · 2017
Later among the works it cites.
Neural language modeling by jointly learning syntax and lexicon
Yikang Shen, Zhouhan Lin, Chin-Wei Huang, and Aaron Courville · 2017
Later among the works it cites.
The neural network pushdown automaton: Model, stack and learning simulations
Guo-Zheng Sun, C Lee Giles, Hsing-Hen Chen, and Yee-Chun Lee · 2017
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2017
Later among the works it cites.
Sequence-to-dependency neural machine translation
Shuangzhi Wu, Dongdong Zhang, Nan Yang, Mu Li, and Ming Zhou · 2017
Later among the works it cites.
Breaking the softmax bottleneck: A high-rank rnn language model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, and William W Cohen · 2017
Later among the works it cites.
Generative neural machine for tree structures
Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen, and Qing He · 2017
Later among the works it cites.
Learning to compose task-specific tree structures
Jihun Choi, Kang Min Yoo, and Sang-goo Lee · 2018
Closest in time.
Colorless green recurrent networks dream hierarchically
Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, and Marco Baroni · 2018
Closest in time.
Grammar induction with neural language models: An unusual replication
Phu Mon Htut, Kyunghyun Cho, and Samuel R Bowman · 2018
Closest in time.
Learning hierarchical structures on-the-fly with a recurrent-recursive model for sequences
Athul Paul Jacob, Zhouhan Lin, Alessandro Sordoni, and Yoshua Bengio · 2018
Closest in time.
Lstms can learn syntax-sensitive dependencies well, but modeling structure makes them better
Adhiguna Kuncoro, Chris Dyer, John Hale, Dani Yogatama, Stephen Clark, and Phil Blunsom · 2018
Closest in time.
Targeted syntactic evaluation of language models
Rebecca Marvin and Tal Linzen · 2018
Closest in time.
On tree-based neural sentence modeling
Haoyue Shi, Hao Zhou, Jiaze Chen, and Lei Li · 2018
Closest in time.
Do latent tree learning models identify meaningful structure in sentences?
Adina Williams, Andrew Drozdov*, and Samuel R Bowman · 2018
Closest in time.
Memory architectures in recurrent neural network language models
Dani Yogatama, Yishu Miao, Gabor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer, and Phil Blunsom · 2018
Closest in time.
The emergence of number and syntax units in lstm language models
Yair Lakretz, German Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, and Marco Baroni · 2019
Closest in time.