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Deep learning models for semantics are generally evaluated using naturalistic corpora.
HELP: A dataset for identifying shortcomings of neural models in monotonicity reasoning
Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui, Satoshi Sekine, Lasha Abzianidze, and Johan Bos. 2019 · 1904
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Finding structure in time
Jeffrey L. Elman. 1990 · 1990
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Studies on Natural Logic and Categorial Grammar
V.M.S. Sánchez-Valencia. 1991 · 1991
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Natural logic for textual inference
Bill MacCartney and Christopher D. Manning. 2007 · 2007
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A brief history of natural logic
Johan van Benthem. 2008 · 2008
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An extended model of natural logic
Bill MacCartney and Christopher D. Manning. 2009 · 2009
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Prover9 and Mace4
W. McCune. 2005–2010 · 2010
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Can recursive neural tensor networks learn logical reasoning?
Samuel R. Bowman. 2013 · 2013
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Recent progress on monotonicity
Thomas F. Icard and Lawrence S. Moss. 2013 · 2013
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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 · 2013
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015a · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Cited alongside, same era.
Can neural networks understand logical entailment?
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, and Edward Grefenstette. 2018 · 2018
Later among the works it cites.
Stress-testing neural models of natural language inference with multiply-quantified sentences
Atticus Geiger, Ignacio Cases, Lauri Karttunen, and Christopher Potts. 2018 · 2018
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Breaking nli systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
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Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, and Phil Blunsom. 2015 · 2015
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Brenden M. Lake and Marco Baroni. 2017 · 2017
Cited alongside, same era.
Can neural networks learn logical reasoning?
Sara Veldhoen and Willem Zuidema. 2018 · 2017
Cited alongside, same era.
Evaluating compositionality in sentence embeddings
Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel J. Gershman, and Noah D. Goodman. 2018 · 2018
Cited alongside, same era.
Recursive neural networks can learn logical semantics
Samuel R. Bowman, Christopher Potts, and Christopher D. Manning. 2015b
Cited in the paper.
Parsing natural scenes and natural language with recursive neural networks
Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, and Christopher D. Manning. 2011a
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Semi-supervised recursive autoencoders for predicting sentiment distributions
Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011b
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Analyzing compositionality-sensitivity of NLI models
Yixin Nie, Yicheng Wang, and Mohit Bansal. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Performance impact caused by hidden bias of training data for recognizing textual entailment
Masatoshi Tsuchiya. 2018 · 2018
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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