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Transformers have been shown to emulate logical deduction over natural language theories (logical rules expressed in natural language), reliably assigning true/false labels to candidate implications.
Programs with common sense
John W. McCarthy. 1959 · 1959
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Applications of circumscription to formalizing common sense knowledge
John McCarthy. 1984 · 1984
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Of brittleness and bottlenecks: Challenges in the creation of pattern-recognition and expert-system models
Mark A Musen and Johan Van der Lei. 1988 · 1988
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What you always wanted to know about datalog (and never dared to ask)
S. Ceri, G. Gottlob, and L. Tanca. 1989 · 1989
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Interpretation as abduction
J. Hobbs, Mark E. Stickel, Douglas E. Appelt, and P. Martin. 1993 · 1993
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Abductive theories in artificial intelligence
K. Konolige. 1997 · 1997
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WT5?! training text-to-text models to explain their predictions
Sharan Narang, Colin Raffel, Katherine Lee, A. Roberts, Noah Fiedel, and Karishma Malkan. 2020 · 2004
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Generative language modeling for automated theorem proving
Stanislas Polu and Ilya Sutskever. 2020 · 2009
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End-to-end differentiable proving
Tim Rocktäschel and S. Riedel. 2017 · 2017
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e-SNLI: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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ERASER: A benchmark to evaluate rationalized nlp models
Jay DeYoung, Sarthak Jain, Nazneen Rajani, E. Lehman, Caiming Xiong, R. Socher, and Byron C. Wallace. 2019 · 2019
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A survey on semantic parsing
Aishwarya Kamath and Rajarshi Das. 2019 · 2019
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Language models as knowledge bases?
F. Petroni, Tim Rocktäschel, Patrick Lewis, A. Bakhtin, Y. Wu, Alexander H. Miller, and S. Riedel. 2019 · 2019
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Learning a SAT solver from single-bit supervision
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L. Dill. 2019 · 2019
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Nlprolog: Reasoning with weak unification for question answering in natural language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel. 2019 · 2019
Measuring systematic generalization in neural proof generation with transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy, and C. Pal. 2020 · 2020
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R4C: A benchmark for evaluating RC systems to get the right answer for the right reason
N. Inoue, Pontus Stenetorp, and Kentaro Inui. 2020 · 2020
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Learning to explain: Datasets and models for identifying valid reasoning chains in multihop question-answering
Harsh Jhamtani and P. Clark. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, M. Matena, Yanqi Zhou, W. Li, and Peter J. Liu. 2020 · 2020
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PRover: Proof generation for interpretable reasoning over rules
Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal. 2020 · 2020
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, S. Yih, and Yejin Choi. 2020 · 2020
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Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020 · 2020
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Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, and Matt Gardner. 2020 · 2020
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Alon Talmor, Oyvind Tafjord, P. Clark, Y. Goldberg, and Jonathan Berant. 2020 · 2020
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Learning to prove theorems by learning to generate theorems
Ming-Zhe Wang and Jun Deng. 2020 · 2020
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