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There is mounting evidence that existing neural network models, in particular the very popular sequence-to-sequence architecture, struggle to systematically generalize to unseen compositions of seen components.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019 · 1907
Earlier work this paper cites.
Die Grundlagen der Arithmetik (The Foundations of Arithmetic): eine logisch- mathematische Untersuchung ber den Begriff der Zahl
Gottlob Frege. 1884 · 1961
Earlier work this paper cites.
Universal grammar
Richard Montague. 1970 · 1970
Earlier work this paper cites.
Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn. 1988 · 1988
Earlier work this paper cites.
Lexical semantics and compositionality
Barbara Partee. 1995 · 1995
Earlier work this paper cites.
The Algebraic Mind: Integrating Connectionism and Cognitive Science
Gary F. Marcus. 2003 · 2003
Earlier work this paper cites.
Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
Daniel Furrer, Marc van Zee, Nathan Scales, and Nathanael Schärli. 2020 · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
Earlier work this paper cites.
Aspects of the Theory of Syntax , volume 11
Noam Chomsky. 2014 · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Language to logical form with neural attention
Li Dong and Mirella Lapata. 2016 · 2016
Earlier work this paper cites.
Data recombination for neural semantic parsing
Robin Jia and Percy Liang. 2016 · 2016
Earlier work this paper cites.
A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
Earlier work this paper cites.
beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
Cited alongside, same era.
LSDSem 2017 shared task: The story cloze test
Nasrin Mostafazadeh, Michael Roth, Annie Louis, Nathanael Chambers, and James Allen. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Ricky T. Q. Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud. 2018 · 2018
Cited alongside, same era.
Towards robust neural machine translation
Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, and Yang Liu. 2018 · 2018
Cited alongside, same era.
Improving text-to-SQL evaluation methodology
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Yinuo Guo, Zeqi Lin, Jian-Guang Lou, and Dongmei Zhang. 2020 · 2020
Later among the works it cites.
Improve transformer models with better relative position embeddings
Zhiheng Huang, Davis Liang, Peng Xu, and Bing Xiang. 2020 · 2020
Later among the works it cites.
Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, and Olivier Bousquet. 2020 · 2020
Later among the works it cites.
COGS: A compositional generalization challenge based on semantic interpretation
Najoung Kim and Tal Linzen. 2020 · 2020
Later among the works it cites.
Improving compositional generalization in semantic parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, and Jonathan Berant. 2020 · 2020
Later among the works it cites.
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Cited alongside, same era.
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Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Later among the works it cites.
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