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Abductive reasoning is inference to the most plausible explanation.
Linguistics and natural logic
George Lakoff · 1970
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Abductive and deductive change
Henning Andersen · 1973
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Scripts, plans, and knowledge
Roger C. Schank and Robert P. Abelson · 1975
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Inference in text understanding
Peter Norvig · 1987
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Interpretation as abduction
Jerry R. Hobbs, Mark Stickel, Paul Martin, and Douglas Edwards · 1988
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Probabilistic semantics for cost based abduction
Eugene Charniak and Solomon Eyal Shimony · 1990
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The extraordinary ordinary powers of abductive reasoning
Gary Shank · 1998
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Abductive inference: Computation , philosophy , technology
Susan G. Josephson · 2000
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Reasoning with cause and effect
Judea Pearl · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Robust textual inference via learning and abductive reasoning
Rajat Raina, Andrew Y Ng, and Christopher D Manning · 2005
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2006
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On the role of lexical and world knowledge in rte3
Peter E. Clark, Philip Harrison, John A. Thompson, William R. Murray, Jerry R. Hobbs, and Christiane Fellbaum · 2007
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Natural logic for textual inference
Bill MacCartney and Christopher D. Manning · 2007
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Unsupervised learning of narrative schemas and their participants
Nathanael Chambers and Dan Jurafsky · 2009
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An extended model of natural logic
Bill MacCartney and Christopher D. Manning · 2009
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The winograd schema challenge
Hector J. Levesque, Ernest Davis, and Leora Morgenstern · 2011
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Skip n-grams and ranking functions for predicting script events
Bram Jans, Steven Bethard, Ivan Vulić, and Marie-Francine Moens · 2012
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Resolving complex cases of definite pronouns: The winograd schema challenge
Altaf Rahman and Vincent Ng · 2012
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Statistical script learning with multi-argument events
Karl Pichotta and Raymond Mooney · 2014
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The Book of Why: The New Science of Cause and Effect
Judea Pearl and Dana Mackenzie · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Improving language understanding by generative pre-training
Alec Radford · 2018
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Performance impact caused by hidden bias of training data for recognizing textual entailment
Masatoshi Tsuchiya · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
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A large annotated corpus for learning natural language inference
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One hundred challenge problems for logical formalizations of commonsense psychology
Nicole Maslan, Melissa Roemmele, and Andrew S. Gordon · 2015
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Script induction as language modeling
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Cider: Consensus-based image description evaluation
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A corpus and cloze evaluation for deeper understanding of commonsense stories
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Enhanced lstm for natural language inference
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Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
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Swag: A large-scale adversarial dataset for grounded commonsense inference
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Comet: Commonsense transformers for automatic knowledge graph construction
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Hypothesis only baselines in natural language inference
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Types of common-sense knowledge needed for recognizing textual entailment
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