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A growing body of work makes use of probing to investigate the working of neural models, often considered black boxes.
Assessing bert’s syntactic abilities
Yoav Goldberg. 2019 · 1901
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Explaining classifiers with causal concept effect (cace)
Yash Goyal, Uri Shalit, and Been Kim. 2019 · 1907
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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. 2019b · 1907
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
Earlier work this paper cites.
Evaluating commonsense in pre-trained language models
Xuhui Zhou, Yue Zhang, Leyang Cui, and Dandan Huang. 2019 · 1911
Earlier work this paper cites.
Modeling by shortest data description
Jorma Rissanen. 1978 · 1978
Earlier work this paper cites.
A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2002
Earlier work this paper cites.
Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov. 2020 · 2003
Earlier work this paper cites.
Investigating transferability in pretrained language models
Alex Tamkin, Trisha Singh, Davide Giovanardi, and Noah Goodman. 2020 · 2004
Earlier work this paper cites.
Causal mediation analysis for interpreting neural nlp: The case of gender bias
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. 2020 · 2004
Earlier work this paper cites.
Probing the probing paradigm: Does probing accuracy entail task relevance?
Abhilasha Ravichander, Yonatan Belinkov, and Eduard Hovy. 2020 · 2005
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, and Vincent Dubourg. 2011 · 2011
Earlier work this paper cites.
Universal dependency annotation for multilingual parsing
Ryan McDonald, Joakim Nivre, Yvonne Quirmbach-Brundage, Yoav Goldberg, Dipanjan Das, Kuzman Ganchev, Keith Hall, Slav Petrov, Hao Zhang, Oscar Täckström, et al. 2013 · 2013
Earlier work this paper cites.
Ontonotes release 5.0 ldc2013t19
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013 · 2013
Earlier work this paper cites.
Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
Cited alongside, same era.
Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information
Mario Giulianelli, Jack Harding, Florian Mohnert, Dieuwke Hupkes, and Willem Zuidema. 2018 · 2018
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Later among the works it cites.
Visualizing and measuring the geometry of bert
Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B Viegas, Andy Coenen, Adam Pearce, and Been Kim. 2019 · 2019
Later among the works it cites.
olmpics – on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant. 2019 · 2019
Later among the works it cites.
Investigating bert’s knowledge of language: Five analysis methods with npis
Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, et al. 2019 · 2019
Later among the works it cites.
Probing linguistic features of sentence-level representations in relation extraction
Christoph Alt, Aleksandra Gabryszak, and Leonhard Hennig. 2020 · 2020
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Visualisation and’diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure
Dieuwke Hupkes, Sara Veldhoen, and Willem Zuidema. 2018 · 2018
Cited alongside, same era.
The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie. 2018 · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Do neural language representations learn physical commonsense?
Maxwell Forbes, Ari Holtzman, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Designing and interpreting probes with control tasks
J. Hewitt and P. Liang. 2019 · 2019
Cited alongside, same era.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
Cited alongside, same era.
Experiment tracking with weights and biases
Lukas Biewald. 2020 · 2020
Closest in time.
Causalm: Causal model explanation through counterfactual language models
Amir Feder, Nadav Oved, Uri Shalit, and Roi Reichart. 2020 · 2020
Closest in time.
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Closest in time.
Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020
Closest in time.
Information-theoretic probing for linguistic structure
Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, Ran Zmigrod, Adina Williams, and Ryan Cotterell. 2020 · 2020
Closest in time.
Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
Closest in time.
Probing neural language models for human tacit assumptions
Nathaniel Weir, Adam Poliak, and Benjamin Van Durme. 2020 · 2020
Closest in time.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
Closest in time.