Fetching the paper…
Reading the bibliography…
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.
S. Ghosh, Giedrius Burachas, Arijit Ray, and Avi Ziskind. 2019 · 1902
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, L Debut, V Sanh, J Chaumond, C Delangue, A Moi, P Cistac, T Rault, R Louf, M Funtowicz, et al. 2019 · 1910
Earlier work this paper cites.
Feature-attention graph convolutional networks for noise resilient learning
M. Shi, Yufei Tang, Xingquan Zhu, and J. Liu. 2019 · 1912
Earlier work this paper cites.
Note on the sampling error of the difference between correlated proportions or percentages
Quinn McNemar. 1947 · 1947
Earlier work this paper cites.
Mental Models
Dedre Gentner and Albert L. Stevens. 1983 · 1983
Earlier work this paper cites.
Mental Models : Towards a Cognitive Science of Language
P. Johnson-Laird. 1983 · 1983
Earlier work this paper cites.
Defeasible reasoning
J. Pollock. 1987 · 1987
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991
Earlier work this paper cites.
Hierarchical mixtures of experts and the em algorithm
Michael I Jordan and Robert A Jacobs. 1994 · 1994
Earlier work this paper cites.
Convergence results for the em approach to mixtures of experts architectures
Michael I Jordan and Lei Xu. 1995 · 1995
Earlier work this paper cites.
Mental models and causal explanation: Judgements of probable cause and explanatory relevance
D. Hilton. 1996 · 1996
Earlier work this paper cites.
A re-examination of text categorization methods
Yiming Yang and Xin Liu. 1999 · 1999
Earlier work this paper cites.
A recursive semantics for defeasible reasoning
J. Pollock. 2009 · 2009
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Defeasible Reasoning
Robert Koons. 2017 · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Cited alongside, same era.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean. 2017 · 2017
Cited alongside, same era.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
The hitchhiker’s guide to testing statistical significance in natural language processing
Rotem Dror, Gili Baumer, Segev Shlomov, and Roi Reichart. 2018 · 2018
Cited alongside, same era.
Explore mixture of experts in graph neural networks
Xuanyu Zhou and Yuanhang Luo. 2019 · 2019
Later among the works it cites.
From ’f’ to ’a’ on the n.y. regents science exams: An overview of the aristo project
P. Clark, Oren Etzioni, Daniel Khashabi, Tushar Khot, B. D. Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Niket Tandon, Sumithra Bhakthavatsalam, Dirk Groeneveld, Michal Guerquin, and Michael Schmitz. 2020 · 2020
Later among the works it cites.
Scalable multi-hop relational reasoning for knowledge-aware question answering
Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, and Xiang Ren. 2020 · 2020
Later among the works it cites.
Social chemistry 101: Learning to reason about social and moral norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jiatao Gu, Hany Hassan, Jacob Devlin, and Victor O.K. Li. 2018 · 2018
Cited alongside, same era.
Open domain question answering using early fusion of knowledge bases and text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William Cohen. 2018 · 2018
Cited alongside, same era.
Reasoning about actions and state changes by injecting commonsense knowledge
Niket Tandon, Bhavana Dalvi, Joel Grus, Wen-tau Yih, Antoine Bosselut, and Peter Clark. 2018 · 2018
Cited alongside, same era.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Multi-source cross-lingual model transfer: Learning what to share
Xilun Chen, Ahmed Hassan Awadallah, Hany Hassan, Wei Wang, and Claire Cardie. 2019 · 2019
Cited alongside, same era.
Pytorch lightning
et al. Falcon, WA. 2019 · 2019
Cited alongside, same era.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2020
Later among the works it cites.
Infusing knowledge into the textual entailment task using graph convolutional networks
Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij P. Fadnis, R. Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, and Achille Fokoue. 2020 · 2020
Later among the works it cites.
Graph-based reasoning over heterogeneous external knowledge for commonsense question answering
Shangwen Lv, Daya Guo, Jingjing Xu, Duyu Tang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, and Songlin Hu. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Thinking like a skeptic: Defeasible inference in natural language
Rachel Rudinger, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Unsupervised commonsense question answering with self-talk
Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
Later among the works it cites.
Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering
Antoine Bosselut, Ronan Le Bras, and Yejin Choi. 2021 · 2021
Closest in time.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer. 2021 · 2021
Closest in time.
Graph classification by mixture of diverse experts
Fenyu Hu, Liping Wang, Shu Wu, Liang Wang, and Tieniu Tan. 2021 · 2021
Closest in time.
Knowledge-driven data construction for zero-shot evaluation in commonsense question answering
Kaixin Ma, Filip Ilievski, Jonathan Francis, Yonatan Bisk, Eric Nyberg, and Alessandro Oltramari. 2021 · 2021
Closest in time.
Could you give me a hint ? generating inference graphs for defeasible reasoning
Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, and Eduard Hovy. 2021 · 2021
Closest in time.
proscript: Partially ordered scripts generation via pre-trained language models
Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark, and Yejin Choi. 2021 · 2021
Closest in time.