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Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of the capability of these models.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 1901
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Scibert: Pretrained contextualized embeddings for scientific text
Iz Beltagy, Arman Cohan, and Kyle Lo. 2019 · 1903
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How to fine-tune BERT for text classification?
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2019 · 1905
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XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 1906
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar S. Joshi, Danqi Chen, Omer Levy, Miranda Paige Linscott Lewis, Luke S. Zettlemoyer, and Veselin Stoyanov. 2019b · 1907
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WINOGRANDE: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1907
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Programs with common sense
Jeanette McCarthy. 1960 · 1960
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A study on similarity and relatedness using distributional and wordnet-based approaches
Eneko Agirre, Enrique Alfonseca, Keith B. Hall, Jana Kravalova, Marius Pasca, and Aitor Soroa. 2009 · 2009
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon. 2011 · 2011
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Distributional semantics in technicolor
Elia Bruni, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. 2012 · 2012
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Reporting bias and knowledge acquisition
Jonathan Gordon and Benjamin van Durme. 2013 · 2013
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Conceptnet 5: A large semantic network for relational knowledge
R. Speer and Catherine Havasi. 2013 · 2013
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The centre for speech, language and the brain (CSLB) concept property norms
Barry J. Devereux, Lorraine K. Tyler, Jeroen Geertzen, and Billi Randall. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Commonsense reasoning and commonsense knowledge in artificial intelligence
Ernest Davis and Gary Marcus. 2015 · 2015
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What kinds of knowledge are needed for genuine understanding?
Lenhart Schubert. 2015 · 2015
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Virtual embodiment: A scalable long-term strategy for artificial intelligence research
MCScript: A novel dataset for assessing machine comprehension using script knowledge
Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, and Manfred Pinkal. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 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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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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Douwe Kiela, Luana Bulat, Anita L. Vero, and Stephen Clark. 2016 · 2016
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A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James F. Allen. 2016 · 2016
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RACE: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard H. Hovy. 2017 · 2017
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Li Lucy and Jon Gauthier. 2017 · 2017
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WebChild 2.0 : Fine-grained commonsense knowledge distillation
Niket Tandon, Gerard de Melo, and Gerhard Weikum. 2017 · 2017
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Ordinal common-sense inference
Sheng Zhang, Rachel Rudinger, Kevin Duh, and Benjamin Van Durme. 2017 · 2017
Cited alongside, same era.
Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matthew Ph Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019a
Cited in the paper.
Maxwell Forbes, Ari Holtzman, and Yejin Choi. 2019 · 2019
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MCScript2.0: A machine comprehension corpus focused on script events and participants
Simon Ostermann, Michael Roth, and Manfred Pinkal. 2019 · 2019
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ATOMIC: An atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi. 2019 · 2019
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HellaSwag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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ERNIE: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
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