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Commonsense question answering (CQA) aims to test if models can answer questions regarding commonsense knowledge that everyone knows.
Good question! statistical ranking for question generation
Heilman, M.; and Smith, N. A. 2010 · 2010
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Wikidata: a free collaborative knowledgebase
Vrandečić, D.; and Krötzsch, M. 2014 · 2014
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ConceptNet 5.5: An open multilingual graph of general knowledge
Speer, R.; Chin, J.; and Havasi, C. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Commonsense for Generative Multi-Hop Question Answering Tasks
Bauer, L.; Wang, Y.; and Bansal, M. 2018 · 2018
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Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples
Joshi, V.; Peters, M.; and Hopkins, M. 2018 · 2018
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Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
Mihaylov, T.; Clark, P.; Khot, T.; and Sabharwal, A. 2018 · 2018
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SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
Zellers, R.; Bisk, Y.; Schwartz, R.; and Choi, Y. 2018 · 2018
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Dynamic knowledge graph construction for zero-shot commonsense question answering
Bosselut, A.; Le Bras, R.; and Choi, Y. 2019 · 2019
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COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
Bosselut, A.; Rashkin, H.; Sap, M.; Malaviya, C.; Celikyilmaz, A.; and Choi, Y. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z.; Chen, M.; Goodman, S.; Gimpel, K.; Sharma, P.; and Soricut, R. 2019 · 2019
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Unsupervised Question Answering by Cloze Translation
Lewis, P.; Denoyer, L.; and Riedel, S. 2019 · 2019
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KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Lin, B. Y.; Chen, X.; Chen, J.; and Ren, X. 2019 · 2019
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RoBERTa: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 2019
Cited alongside, same era.
Language Models as Knowledge Bases?
Petroni, F.; Rocktäschel, T.; Riedel, S.; Lewis, P.; Bakhtin, A.; Wu, Y.; and Miller, A. 2019 · 2019
Cited alongside, same era.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Talmor, A.; Herzig, J.; Lourie, N.; and Berant, J. 2019 · 2019
Cited alongside, same era.
Align, mask and select: A simple method for incorporating commonsense knowledge into language representation models
Ye, Z.-X.; Chen, Q.; Wang, W.; and Ling, Z.-H. 2019 · 2019
Cited alongside, same era.
From recognition to cognition: Visual commonsense reasoning
Knowledge-driven Self-supervision for Zero-shot Commonsense Question Answering
Ma, K.; Ilievski, F.; Francis, J.; Bisk, Y.; Nyberg, E.; and Oltramari, A. 2020 · 2020
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GECToR – Grammatical Error Correction: Tag, Not Rewrite
Omelianchuk, K.; Atrasevych, V.; Chernodub, A.; and Skurzhanskyi, O. 2020 · 2020
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How Much Knowledge Can You Pack into the Parameters of a Language Model?
Roberts, A.; Raffel, C.; and Shazeer, N. 2020 · 2020
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Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K.; Le Bras, R.; Bhagavatula, C.; and Choi, Y. 2020 · 2020
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Unsupervised Commonsense Question Answering with Self-Talk
Shwartz, V.; West, P.; Le Bras, R.; Bhagavatula, C.; and Choi, Y. 2020 · 2020
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oLMpics-On What Language Model Pre-training Captures
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Zellers, R.; Bisk, Y.; Farhadi, A.; and Choi, Y. 2019 · 2019
Cited alongside, same era.
“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding
Zhou, B.; Khashabi, D.; Ning, Q.; and Roth, D. 2019 · 2019
Cited alongside, same era.
Piqa: Reasoning about physical commonsense in natural language
Bisk, Y.; Zellers, R.; Gao, J.; Choi, Y.; et al. 2020 · 2020
Cited alongside, same era.
Language models are few-shot learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2020
Cited alongside, same era.
How can we know what language models know?
Jiang, Z.; Xu, F. F.; Araki, J.; and Neubig, G. 2020 · 2020
Cited alongside, same era.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
Cited alongside, same era.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
Lin, B. Y.; Zhou, W.; Shen, M.; Zhou, P.; Bhagavatula, C.; Choi, Y.; and Ren, X. 2020 · 2020
Cited alongside, same era.
Talmor, A.; Elazar, Y.; Goldberg, Y.; and Berant, J. 2020 · 2020
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Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
Wang, P.; Peng, N.; Ilievski, F.; Szekely, P.; and Ren, X. 2020 · 2020
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Evaluating commonsense in pre-trained language models
Zhou, X.; Zhang, Y.; Cui, L.; and Huang, D. 2020 · 2020
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Making Pre-trained Language Models Better Few-shot Learners
Gao, T.; Fisch, A.; and Chen, D. 2021 · 2021
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Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; and Neubig, G. 2021 · 2021
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Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference
Schick, T.; and Schütze, H. 2021 · 2021
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Multi-view Subword Regularization
Wang, X.; Ruder, S.; and Neubig, G. 2021 · 2021
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Fusing Context Into Knowledge Graph for Commonsense Reasoning
Xu, Y.; Zhu, C.; Xu, R.; Liu, Y.; Zeng, M.; and Huang, X. 2021 · 2021
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