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Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.
Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 1909
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A metric for distributions with applications to image databases
Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas. 1998 · 1998
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Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2004
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Information-theoretic probing for linguistic structure
Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, Ran Zmigrod, Adina Williams, and Ryan Cotterell. 2020 · 2004
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Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren. 2020 · 2005
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Probing for semantic evidence of composition by means of simple classification tasks
Allyson Ettinger, Ahmed Elgohary, and Philip Resnik. 2016 · 2016
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Does string-based neural mt learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
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Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016 · 2016
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Cramér–von mises distance: probabilistic interpretation, confidence intervals, and neighbourhood-of-model validation
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Verb physics: Relative physical knowledge of actions and objects
Maxwell Forbes and Yejin Choi. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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How large are lions? inducing distributions over quantitative attributes
Yanai Elazar, Abhijit Mahabal, Deepak Ramachandran, Tania Bedrax-Weiss, and Dan Roth. 2019 · 2019
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Justifying recommendations using distantly-labeled reviews and fined-grained aspects
Julian McAuley Jianmo Ni, Jiacheng Li. 2019 · 2019
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Transferable adversarial training: A general approach to adapting deep classifiers
Hong Liu, Mingsheng Long, Jianmin Wang, and Michael I. Jordan. 2019 · 2019
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Do NLP models know numbers? probing numeracy in embeddings
Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner. 2019 · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
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Temporal common sense acquisition with minimal supervision
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Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Extracting commonsense properties from embeddings with limited human guidance
Yiben Yang, Larry Birnbaum, Ji-Ping Wang, and Doug Downey. 2018 · 2018
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Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth. 2020 · 2020
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