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Logical reasoning is needed in a wide range of NLP tasks.
What can neural networks reason about?
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2019 · 1905
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Reasoning over paragraph effects in situations
Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner. 2019 · 1908
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Improving graph neural network representations of logical formulae with subgraph pooling
Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth D. Forbus, and Achille Fokoue. 2019 · 1911
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An experimental study of formula embeddings for automated theorem proving in first-order logic
Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse, and Achille Fokoue. 2020 · 2002
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross B. Girshick. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 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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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
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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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Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Analysing mathematical reasoning abilities of neural models
Deep learning for symbolic mathematics
Guillaume Lample and François Charton. 2020 · 2020
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Logiqa: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2020 · 2020
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Leap-of-thought: Teaching pre-trained models to systematically reason over implicit knowledge
Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, and Jonathan Berant. 2020 · 2020
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Reclor: A reading comprehension dataset requiring logical reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng. 2020 · 2020
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Yanai Elazar, Hongming Zhang, Yoav Goldberg, and Dan Roth. 2021 · 2021
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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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CLUTRR: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton. 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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Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L. Donti, Bryan Wilder, and J. Zico Kolter. 2019 · 2019
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Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020 · 2020
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Measuring systematic generalization in neural proof generation with transformers
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Teaching temporal logics to neural networks
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Wilds: A benchmark of in-the-wild distribution shifts
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Diagnosing the first-order logical reasoning ability through logicnli
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On calibration and out-of-domain generalization
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Naturalproofs: Mathematical theorem proving in natural language
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Broaden the vision: Geo-diverse visual commonsense reasoning
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