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Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
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Knowledge fusion and semantic knowledge ranking for open domain question answering
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Conceptnet—a practical commonsense reasoning tool-kit
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Fusing context into knowledge graph for commonsense reasoning
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Translating embeddings for modeling multi-relational data
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Compositional vector space models for knowledge base completion
Arvind Neelakantan, Benjamin Roth, and Andrew McCallum. 2015 · 2015
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Chains of reasoning over entities, relations, and text using recurrent neural networks
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, and Tim Lillicrap. 2017 · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. 2018 · 2018
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Machine common sense concept paper
David Gunning. 2018 · 2018
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Neural relational inference for interacting systems
Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard S. Zemel. 2018 · 2018
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Deep learning: A critical appraisal
Gary Marcus. 2018 · 2018
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KagNet: Knowledge-aware graph networks for commonsense reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xiang Ren. 2019 · 2019
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Towards generalizable neuro-symbolic systems for commonsense question answering
Kaixin Ma, Jonathan Francis, Quanyang Lu, Eric Nyberg, and Alessandro Oltramari. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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PullNet: Open domain question answering with iterative retrieval on knowledge bases and text
Haitian Sun, Tania Bedrax-Weiss, and William Cohen. 2019 · 2019
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Can a suit of armor conduct electricity? a new dataset for open book question answering
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Learning conditioned graph structures for interpretable visual question answering
Will Norcliffe-Brown, Stathis Vafeias, and Sarah Parisot. 2018 · 2018
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Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018b · 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.
CODAH: An adversarially-authored question answering dataset for common sense
Michael Chen, Mike D’Arcy, Alisa Liu, Jared Fernandez, and Doug Downey. 2019 · 2019
Cited alongside, same era.
Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander Rush. 2019 · 2019
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Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Graph-revised convolutional network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang. 2019 · 2019
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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
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On the variance of the adaptive learning rate and beyond
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Graph-based reasoning over heterogeneous external knowledge for commonsense question answering
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Connecting the dots: A knowledgeable path generator for commonsense question answering
Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, and Xiang Ren. 2020 · 2020
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QA-GNN: Reasoning with language models and knowledge graphs for question answering
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