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Programming machines with commonsense reasoning (CSR) abilities is a longstanding challenge in the Artificial Intelligence community.
How Additional Knowledge can Improve Natural Language Commonsense Question Answering?
A. Mitra, P. Banerjee, K. K. Pal, S. Mishra, and C. Baral · 1909
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The naive physics manifesto
P. Hayes · 1978
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Towards a general theory of action and time
J. F. Allen · 1984
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Formal Theories of the Commonsense World
J. R. Hobbs and R. C. Moore · 1985
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Representations of Commonsense Knowledge
E. Davis · 1990
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What’s basic about basic emotions?
A. Ortony and T. J. Turner · 1990
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Cyc: A large-scale investment in knowledge infrastructure
D. B. Lenat · 1995
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Bleu: a method for automatic evaluation of machine translation
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu · 2002
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The Seventh PASCAL Recognizing Textual Entailment Challenge
L. Bentivogli, P. Clark, I. Dagan, and D. Giampiccolo · 2011
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The Emotion Ontology: Enabling Interdisciplinary Research in the Affective Sciences
J. Hastings, W. Ceusters, B. Smith, and K. Mulligan · 2011
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The Units Ontology: a tool for integrating units of measurement in science
G. V. Gkoutos, P. N. Schofield, and R. Hoehndorf · 2012
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W3C xml schema definition language (XSD) 1.1 part 2: Datatypes
A. Malhotra, S. Gao, M. Sperberg-McQueen, H. Thompson, D. Peterson, and P. V. Biron · 2012
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OGC GeoSPARQL-A geographic query language for RDF data
M. Perry and J. Herring · 2012
Cited alongside, same era.
PROV-o: The PROV ontology
D. McGuinness, T. Lebo, and S. Sahoo · 2013
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The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery
M. Dumontier, C. J. Baker, J. Baran, A. Callahan, L. Chepelev, J. Cruz-Toledo, N. R. Del Rio, G. Duck, L. I. Furlong, N. Keath, D. Klassen, J. P. McCusker, N. Queralt-Rosinach, M. Samwald, N. Villanueva-Rosales, M. D. Wilkinson, and R. Hoehndorf · 2014
Cited alongside, same era.
Commonsense reasoning and commonsense knowledge in artificial intelligence
E. Davis and G. Marcus · 2015
Cited alongside, same era.
A Formal Theory of Commonsense Psychology: How People Think People Think
A. S. Gordon and J. R. Hobbs · 2017
Cited alongside, same era.
DARPA Machine Common Sense (MCS) Broad Agency Announcement, 2018
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Do fine-tuned commonsense language models really generalize?
M. Kejriwal and K. Shen · 2020
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Time ontology in OWL
C. Little and S. Cox · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
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2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
L. Huang, R. Le Bras, C. Bhagavatula, and Y. Choi · 2019
Cited alongside, same era.
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
M. Sap, R. L. Bras, E. Allaway, C. Bhagavatula, N. Lourie, H. Rashkin, B. Roof, N. A. Smith, and Y. Choi · 2019
Cited alongside, same era.
Social IQa: Commonsense Reasoning about Social Interactions
M. Sap, H. Rashkin, D. Chen, R. Le Bras, and Y. Choi · 2019
Cited alongside, same era.
Superglue: A stickier benchmark for general-purpose language understanding systems
A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman · 2019
Cited alongside, same era.
Deep Adversarial Learning for NLP
W. Y. Wang, S. Singh, and J. Li · 2019
Cited alongside, same era.
Back to square one: Artifact detection, training and commonsense disentanglement in the Winograd schema
Y. Elazar, H. Zhang, Y. Goldberg, and D. Roth · 2021
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Data and its (dis)contents: A survey of dataset development and use in machine learning research
A. Paullada, I. D. Raji, E. M. Bender, E. Denton, and A. Hanna · 2021
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Exploring and Analyzing Machine Commonsense Benchmarks
H. Santos, M. Gordon, Z. Liang, G. Forbush, and D. L. McGuinness · 2021
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An experimental study measuring human annotator categorization agreement on commonsense sentences
H. Santos, M. Kejriwal, A. M. Mulvehill, G. Forbush, and D. L. McGuinness · 2021
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Toward a New Science of Common Sense
R. J. Brachman and H. J. Levesque · 2022
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Designing a strong test for measuring true common-sense reasoning
M. Kejriwal, H. Santos, A. M. Mulvehill, and D. L. McGuinness · 2022
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Multitask Prompted Training Enables Zero-Shot Task Generalization
V. Sanh, A. Webson, C. Raffel, S. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, T. L. Scao, A. Raja, M. Dey, M. S. Bari, C. Xu, U. Thakker, S. Sharma, E. Szczechla, T. Kim, G. Chhablani, N. V. Nayak, D. Datta, J. Chang, M. Jiang, H. Wang, M. Manica, S. Shen, Z.-X. Yong, H. Pandey, M. Mckenna, R. Bawden, T. Wang, T. Neeraj, J. Rozen, A. Sharma, A. Santilli, T. Fevry, J. Fries, R. Teehan, T. Bers, S. Biderman, L. Gao, T. Wolf, and A. Rush · 2022
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