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Reasoning over natural language is a challenging problem in NLP.
RoBERTa: A robustly optimized BERT pretraining approach
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Tensor product variable binding and the representation of symbolic structures in connectionist systems
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Handbook of automated reasoning , volume 1
Alan JA Robinson and Andrei Voronkov. 2001 · 2001
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Artificial intelligence: a modern approach
Stuart Russell and Peter Norvig. 2002 · 2002
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Generative language modeling for automated theorem proving
Stanislas Polu and Ilya Sutskever. 2020 · 2009
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The probabilistic relevance framework: BM25 and beyond
Stephen Robertson, Hugo Zaragoza, et al. 2009 · 2009
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First-order theorem proving and Vampire
Laura Kovács and Andrei Voronkov. 2013 · 2013
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A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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Higher-order logical inference with compositional semantics
Koji Mineshima, Pascual Martínez-Gómez, Yusuke Miyao, and Daisuke Bekki. 2015 · 2015
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Combining natural logic and shallow reasoning for question answering
Gabor Angeli, Neha Nayak, and Christopher D Manning. 2016 · 2016
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Reasoning in vector space: An exploratory study of question answering
Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, and Paul Smolensky. 2016 · 2016
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Natural language inference from multiple premises
Alice Lai, Yonatan Bisk, and Julia Hockenmaier. 2017 · 2017
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TensorLog: Deep learning meets probabilistic databases
William W Cohen Fan Yang Kathryn and Rivard Mazaitis. 2018 · 2018
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Diverse Beam Search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 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 Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Explore, propose, and assemble: An interpretable model for multi-hop reading comprehension
Yichen Jiang, Nitish Joshi, Yen-Chun Chen, and Mohit Bansal. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 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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NLProlog: Reasoning with weak unification for question answering in natural language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel. 2019 · 2019
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Learning to prove theorems via interacting with proof assistants
Kaiyu Yang and Jia Deng. 2019 · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
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Transformers as soft reasoners over language
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Édouard Grave. 2021 · 2021
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Explainable multi-hop verbal reasoning through internal monologue
Zhengzhong Liang, Steven Bethard, and Mihai Surdeanu. 2021 · 2021
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Scaling language models: Methods, analysis & insights from training Gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. 2021 · 2021
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Probabilistic graph reasoning for natural proof generation
Changzhi Sun, Xinbo Zhang, Jiangjie Chen, Chun Gan, Yuanbin Wu, Jiaze Chen, Hao Zhou, and Lei Li. 2021 · 2021
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ProofWriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2021 · 2021
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Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020 · 2020
Cited alongside, same era.
Measuring systematic generalization in neural proof generation with transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy, and Chris Pal. 2020 · 2020
Cited alongside, same era.
HoVer: A dataset for many-hop fact extraction and claim verification
Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, and Mohit Bansal. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
A benchmark for systematic generalization in grounded language understanding
Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, and Brenden M Lake. 2020 · 2020
Cited alongside, same era.
PRover: Proof generation for interpretable reasoning over rules
Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal. 2020 · 2020
Cited alongside, same era.
BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
Cited alongside, same era.
Learning symbolic rules for reasoning in quasi-natural language
Kaiyu Yang and Jia Deng. 2021 · 2021
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Natural language deduction through search over statement compositions
Kaj Bostrom, Zayne Sprague, Swarat Chaudhuri, and Greg Durrett. 2022 · 2022
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Towards teachable reasoning systems
Bhavana Dalvi, Oyvind Tafjord, and Peter Clark. 2022 · 2022
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MetGen: A module-based entailment tree generation framework for answer explanation
Ruixin Hong, Hongming Zhang, Xintong Yu, and Changshui Zhang. 2022 · 2022
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Braid: Weaving symbolic and neural knowledge into coherent logical explanations
Aditya Kalyanpur, Tom Breloff, David Ferrucci, Adam Lally, and John Jantos. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Language models of code are few-shot commonsense learners
Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, and Graham Neubig. 2022 · 2022
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Interpretable proof generation via iterative backward reasoning
Hanhao Qu, Yu Cao, Jun Gao, Liang Ding, and Ruifeng Xu. 2022 · 2022
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Entailment tree explanations via iterative retrieval-generation reasoner
Danilo Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henry Zhu, Xinchi Chen, Zhiheng Huang, Peng Xu, Andrew Arnold, et al. 2022 · 2022
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FaiRR: Faithful and robust deductive reasoning over natural language
Soumya Sanyal, Harman Singh, and Xiang Ren. 2022 · 2022
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Towards general natural language understanding with probabilistic worldbuilding
Abulhair Saparov and Tom M Mitchell. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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