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Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life.
Socialiqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019b · 1904
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 1906
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar S. Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke S. Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Schema theory: An information processing model of perception and cognition
Robert Axelrod. 1973 · 1973
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The rectilinear steiner tree problem is np-complete
Michael R Garey and David S. Johnson. 1977 · 1977
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Mental models in cognitive science
Philip N Johnson-Laird. 1980 · 1980
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The winograd schema challenge
Hector J. Levesque. 2011 · 2011
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
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Commonsense reasoning and commonsense knowledge in artificial intelligence
Ernest Davis and Gary Marcus. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Commonsense knowledge base completion
Xiang Li, Aynaz Taheri, Lifu Tu, and Kevin Gimpel. 2016 · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, and Eduard H. Hovy. 2016 · 2016
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Learning what is essential in questions
Daniel Khashabi, Tushar Khot, Ashutosh Sabharwal, and Dan Roth. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard H. Hovy. 2017 · 2017
Cited alongside, same era.
Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov. 2017 · 2017
Cited alongside, same era.
A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, and Timothy P. Lillicrap. 2017 · 2017
Cited alongside, same era.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robert Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Cited alongside, same era.
Webchild 2.0 : Fine-grained commonsense knowledge distillation
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018 · 2018
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A simple method for commonsense reasoning
Trieu H. Trinh and Quoc V. Le. 2018 · 2018
Later among the works it cites.
Automatic extraction of commonsense locatednear knowledge
Frank Xu, Bill Yuchen Lin, and Kenny Q. Zhu. 2018 · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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Niket Tandon, Gerard de Melo, and Gerhard Weikum. 2017 · 2017
Cited alongside, same era.
Dynamic integration of background knowledge in neural nlu systems
Dirk Weissenborn, Tomáš Kočiskỳ, and Chris Dyer. 2017 · 2017
Cited alongside, same era.
Leveraging knowledge bases in lstms for improving machine reading
Bishan Yang and Tom Michael Mitchell. 2017 · 2017
Cited alongside, same era.
Learning beyond datasets: Knowledge graph augmented neural networks for natural language processing
K. M. Annervaz, Somnath Basu Roy Chowdhury, and Ambedkar Dukkipati. 2018 · 2018
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. 2018 · 2018
Cited alongside, same era.
Compositional attention networks for machine reasoning
Drew A. Hudson and Christopher D. Manning. 2018 · 2018
Cited alongside, same era.
Knowledgeable reader: Enhancing cloze-style reading comprehension with external commonsense knowledge
Todor Mihaylov and Anette Frank. 2018 · 2018
Cited alongside, same era.
Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018 · 2018
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Improving question answering by commonsense-based pre-training
Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2018 · 2018
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Do language models have common sense?
Trieu H. Trinh and Quoc V. Le. 2019 · 2019
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From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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