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The term `spurious correlations' has been used in NLP to informally denote any undesirable feature-label correlations.
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. 2019 · 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, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Probabilities of causation: Three counterfactual interpretations and their identification
Judea Pearl. 1999 · 1999
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The class imbalance problem: Significance and strategies
Nathalie Japkowicz. 2000 · 2000
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Causality: Models, Reasoning and Inference , 2nd edition
Judea Pearl. 2009 · 2009
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An introduction to propensity score methods for reducing the effects of confounding in observational studies
Peter C Austin. 2011 · 2011
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
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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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Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
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Repair: Removing representation bias by dataset resampling
Yi Li and Nuno Vasconcelos. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 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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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2019
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What’s in a name? are BERT named entity representations just as good for any other name?
Sriram Balasubramanian, Naman Jain, Gaurav Jindal, Abhijeet Awasthi, and Sunita Sarawagi. 2020 · 2020
An empirical study on robustness to spurious correlations using pre-trained language models
Lifu Tu, Garima Lalwani, Spandana Gella, and He He. 2020 · 2020
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Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov. 2020 · 2020
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Identifying spurious correlations for robust text classification
Zhao Wang and Aron Culotta. 2020 · 2020
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Irm—when it works and when it doesn't: A test case of natural language inference
Yana Dranker, He He, and Yonatan Belinkov. 2021 · 2021
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Explaining black-box algorithms using probabilistic contrastive counterfactuals
Sainyam Galhotra, Romila Pradhan, and Babak Salimi. 2021 · 2021
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Competency problems: On finding and removing artifacts in language data
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TaxiNLI: Taking a ride up the NLU hill
Pratik Joshi, Somak Aditya, Aalok Sathe, and Monojit Choudhury. 2020 · 2020
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End-to-end bias mitigation by modelling biases in corpora
Rabeeh Karimi Mahabadi, Yonatan Belinkov, and James Henderson. 2020 · 2020
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Learning the difference that makes a difference with counterfactually augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton. 2020 · 2020
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Predicting inductive biases of pre-trained models
Charles Lovering, Rohan Jha, Tal Linzen, and Ellie Pavlick. 2020 · 2020
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
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Distributionally robust neural networks
Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. 2020 · 2020
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Matt Gardner, William Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh, and Noah A. Smith. 2021 · 2021
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Debiasing methods in natural language understanding make bias more accessible
Michael Mendelson and Yonatan Belinkov. 2021 · 2021
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Towards unifying feature attribution and counterfactual explanations: Different means to the same end
Ramaravind Kommiya Mothilal, Divyat Mahajan, Chenhao Tan, and Amit Sharma. 2021 · 2021
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Counterfactual invariance to spurious correlations in text classification
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein. 2021 · 2021
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Desiderata for representation learning: A causal perspective
Yixin Wang and Michael I. Jordan. 2021 · 2021
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Uninformative input features and counterfactual invariance: Two perspectives on spurious correlations in natural language
Jacob Eisenstein. 2022 · 2022
Closest in time.
Out-of-distribution generalization in the presence of nuisance-induced spurious correlations
Aahlad Manas Puli, Lily H Zhang, Eric Karl Oermann, and Rajesh Ranganath. 2022 · 2022
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On the limitations of dataset balancing: The lost battle against spurious correlations
Roy Schwartz and Gabriel Stanovsky. 2022 · 2022
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