Fetching the paper…
Reading the bibliography…
Recurrent neural networks empirically generate natural language with high syntactic fidelity.
On the computational power of rnns
Samuel A Korsky and Robert C Berwick. 2019 · 1906
Earlier work this paper cites.
Three models for the description of language
Noam Chomsky. 1956 · 1956
Earlier work this paper cites.
On certain formal properties of grammars
Noam Chomsky. 1959 · 1959
Earlier work this paper cites.
The algebraic theory of context-free languages
Noam Chomsky and Marcel P Schützenberger. 1959 · 1959
Earlier work this paper cites.
Finitary models of language users
George A. Miller and Noam Chomsky. 1963 · 1963
Earlier work this paper cites.
Machine learning using a higher order correlation network
Y C Lee, G Doolen, H H Chen, G Z Sun, T Maxwell, H Y Lee, and C L Giles. 1986 · 1986
Earlier work this paper cites.
Finding structure in time
Jeffrey L Elman. 1990 · 1990
Earlier work this paper cites.
Higher order recurrent networks and grammatical inference
C. Lee Giles, Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee, and Dong Chen. 1990 · 1990
Earlier work this paper cites.
The convergence of mildly context-sensitive grammar formalisms
Aravind K Joshi, K Vijay Shanker, and David Weir. 1990 · 1990
Earlier work this paper cites.
Left-corner parsing and psychological plausibility
Philip Resnik. 1992 · 1992
Earlier work this paper cites.
On the computational power of neural nets
Hava T. Siegelmann and Eduardo D. Sontag. 1992 · 1992
Earlier work this paper cites.
Bounds on the complexity of recurrent neural network implementations of finite state machines
Bill G Horne and Don R Hush. 1994 · 1994
Earlier work this paper cites.
Optimal simulation of automata by neural nets
Piotr Indyk. 1995 · 1995
Earlier work this paper cites.
Constructing deterministic finite-state automata in recurrent neural networks
Christian W Omlin and C Lee Giles. 1996 · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
On the linguistic capacity of real-time counter automata
William. Merrill. 2020 · 2004
Cited alongside, same era.
Constraints on multiple center-embedding of clauses
Fred Karlsson. 2007 · 2007
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Learning to transduce with unbounded memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Inferring algorithmic patterns with stack-augmented recurrent nets
Armand Joulin and Tomas Mikolov. 2015 · 2015
Cited alongside, same era.
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 · 2018
Later among the works it cites.
Long short-term memory as a dynamically computed element-wise weighted sum
Omer Levy, Kenton Lee, Nicholas FitzGerald, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Targeted syntactic evaluation of language models
Rebecca Marvin and Tal Linzen. 2018 · 2018
Later among the works it cites.
Modeling garden path effects without explicit hierarchical syntax
Marten van Schijndel and Tal Linzen. 2018 · 2018
Later among the works it cites.
Evaluating the ability of LSTMs to learn context-free grammars
Luzi Sennhauser and Robert Berwick. 2018 · 2018
Later among the works it cites.
On evaluating the generalization of lstm models in formal languages
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Andrej Karpathy, Justin Johnson, and Li Fei-Fei. 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
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby. 2017 · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017 · 2017
Cited alongside, same era.
Colorless green recurrent networks dream hierarchically
Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Mirac Suzgun, Yonatan Belinkov, and Stuart M Shieber. 2018 · 2018
Later among the works it cites.
On the practical computational power of finite precision rnns for language recognition
Gail Weiss, Yoav Goldberg, and Eran Yahav. 2018 · 2018
Later among the works it cites.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
Later among the works it cites.
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 · 2019
Later among the works it cites.
Sequential neural networks as automata
William Merrill. 2019 · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Later among the works it cites.
Connecting weighted automata and recurrent neural networks through spectral learning
Guillaume Rabusseau, Tianyu Li, and Doina Precup. 2019 · 2019
Later among the works it cites.
LSTM networks can perform dynamic counting
Mirac Suzgun, Yonatan Belinkov, Stuart Shieber, and Sebastian Gehrmann. 2019 · 2019
Later among the works it cites.
Learning the Dyck language with attention-based Seq2Seq models
Xiang Yu, Ngoc Thang Vu, and Jonas Kuhn. 2019 · 2019
Later among the works it cites.
A formal hierarchy of rnn architectures
William Merrill, Gail Weiss, Yoav Goldberg, Roy Schwartz, Noah A Smith, and Eran Yahav. 2020 · 2020
Closest in time.