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Contrastive explanations clarify why an event occurred in contrast to another.
Logic and causal attribution
Denis J Hilton. 1988 · 1986
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Knowledge-based causal attribution: The abnormal conditions focus model
Denis J Hilton and Ben R Slugoski. 1986 · 1986
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The problem of causal selection
Germund Hesslow. 1988 · 1988
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Contrastive explanation
Peter Lipton. 1990 · 1990
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Making Things Happen: A Theory of Causal Explanation
James Woodward. 2003 · 2003
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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 M. Shieber. 2020 · 2004
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. 2013 · 2013
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky. 2016 · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2017 · 2017
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Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, and Max Welling. 2017 · 2017
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AllenNLP: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018 · 2018
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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
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
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Generating counterfactual explanations with natural language
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata. 2018 · 2018
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Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 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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Learning the difference that makes A difference with counterfactually-augmented data
Divyansh Kaushik, Eduard H. Hovy, and Zachary Chase Lipton. 2020 · 2020
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Interpretation of NLP models through input marginalization
Siwon Kim, Jihun Yi, Eunji Kim, and Sungroh Yoon. 2020 · 2020
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Problems with shapley-value-based explanations as feature importance measures
I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle A. Friedler. 2020 · 2020
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Richard Meyes, Constantin Waubert de Puiseau, Andres Posada-Moreno, and Tobias Meisen. 2020 · 2020
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Contrastive explanation: A structural-model approach
Tim Miller. 2020 · 2020
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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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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
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Where’s my head? Definition, data set, and models for numeric fused-head identification and resolution
Yanai Elazar and Yoav Goldberg. 2019 · 2019
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Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 2019
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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. 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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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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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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Contrastive examples for addressing the tyranny of the majority
Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell, and Novi Quadrianto. 2020 · 2020
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Data staining: A method for comparing faithfulness of explainers
Jacob Sippy, Gagan Bansal, and Daniel S Weld. 2020 · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. 2020 · 2020
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Investigating transferability in pretrained language models
Alex Tamkin, Trisha Singh, Davide Giovanardi, and Noah D. Goodman. 2020 · 2020
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Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
Yanai Elazar, Shauli Ravfogel, Alon Jacovi, and Yoav Goldberg. 2021 · 2021
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CausaLM: Causal Model Explanation Through Counterfactual Language Models
Amir Feder, Nadav Oved, Uri Shalit, and Roi Reichart. 2021 · 2021
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Probing the probing paradigm: Does probing accuracy entail task relevance?
Abhilasha Ravichander, Yonatan Belinkov, and Eduard Hovy. 2021 · 2021
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Explaining NLP models via minimal contrastive editing (MiCE)
Alexis Ross, Ana Marasović, and Matthew Peters. 2021 · 2021
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A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
Ilia Stepin, Jose M. Alonso, Alejandro Catala, and Martín Pereira-Fariña. 2021 · 2021
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