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Counterfactual statements describe events that did not or cannot take place.
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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Variabilità e mutabilità (variability and mutability)
Corrado Gini. 1912 · 1912
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Counterfactual statements and logical modality
Bella K Milmed. 1957 · 1957
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Methods of logic
Willard Van Orman Quine. 1982 · 1982
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Classification and regression trees
Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. 1984 · 1984
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Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi. 1987 · 1987
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Support-vector networks
Corinna Cortes and Vladimir Vapnik. 1995 · 1995
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Counterfactual thinking
Neal J Roese. 1997 · 1997
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al. 1999 · 1999
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Random forests
Leo Breiman. 2001 · 2001
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Causal inference based on counterfactuals
M Höfler. 2005 · 2005
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Implications of counterfactual structure for creative generation and analytical problem solving
Keith D Markman, Matthew J Lindberg, Laura J Kray, and Adam D Galinsky. 2007 · 2007
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2009 · 2009
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The interrelationship of time and realis in japanese – in search of the semantic roots of hypothetical meaning
Wesley M. Jacobsen. 2011 · 2011
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Counterfactuals and conditional questions under discussion
Michela Ippolito. 2013 · 2013
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Counterfactuals
David Lewis. 2013 · 2013
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Glove: global vectors for word representation
Jeffery Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Counterfactuals in the language of social media: A natural language processing project in conjunction with the world well being project
Anthony Janocko, Allegra Larche, Joseph Raso, and Kevin Zembroski. 2016 · 2016
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Counterfactual evaluation and learning for search, recommendation and ad placement
Thorsten Joachims and Adith Swaminathan. 2016 · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Cited alongside, same era.
Optimal classifier for imbalanced data using matthews correlation coefficient metric
Sabri Boughorbel, Fethi Jarray, and Mohammed El-Anbari. 2017 · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Cited alongside, same era.
Danushka Bollegala, Ryuichi Kiryo, Kosuke Tsujino, and Haruki Yukawa. 2020 · 2020
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Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings
Rishi Bommasani, Kelly Davis, and Claire Cardie. 2020 · 2020
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Ferryman at semeval-2020 task 5: Optimized bert for detecting counterfactuals
Weilong Chen, Yan Zhuang, Peng Wang, Feng Hong, Yan Wang, and Yanru Zhang. 2020 · 2020
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The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation
Davide Chicco and Giuseppe Jurman. 2020 · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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Recognizing counterfactual thinking in social media texts
Youngseo Son, Anneke Buffone, Joe Raso, Allegra Larche, Anthony Janocko, Kevin Zembroski, H Andrew Schwartz, and Lyle Ungar. 2017 · 2017
Cited alongside, same era.
Learning word meta-embeddings by autoencoding
Danushka Bollegala and Cong Bao. 2018 · 2018
Cited alongside, same era.
Think globally, embed locally — locally linear meta-embedding of words
Danushka Bollegala, Kohei Hayashi, and Ken-ichi Kawarabayashi. 2018 · 2018
Cited alongside, same era.
Challenges of using text classifiers for causal inference
Zach Wood-Doughty, Ilya Shpitser, and Mark Dredze. 2018 · 2018
Cited alongside, same era.
Breaking the softmax bottleneck: A high-rank RNN language model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, and William W. Cohen. 2018 · 2018
Cited alongside, same era.
Counterfactuals in explainable artificial intelligence (xai): evidence from human reasoning
Ruth MJ Byrne. 2019 · 2019
Cited alongside, same era.
Cross-lingual language model pretraining
Alexis Conneau and Guillaume Lample. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Hit-scir at semeval-2020 task 5: Training pre-trained language model with pseudo-labeling data for counterfactuals detection
Xiao Ding, Dingkui Hao, Yuewei Zhang, Kuo Liao, Zhongyang Li, Bing Qin, and Ting Liu. 2020 · 2020
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BUT-FIT at SemEval-2020 task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models
Martin Fajcik, Josef Jon, Martin Docekal, and Pavel Smrz. 2020 · 2020
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Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020
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ALBERT: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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Iterative feature mining for constraint-based data collection to increase data diversity and model robustness
Stefan Larson, Anthony Zheng, Anish Mahendran, Rishi Tekriwal, Adrian Cheung, Eric Guldan, Kevin Leach, and Jonathan K Kummerfeld. 2020 · 2020
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Iscas at semeval-2020 task 5: Pre-trained transformers for counterfactual statement modeling
Yaojie Lu, Annan Li, Hongyu Lin, Xianpei Han, and Le Sun. 2020 · 2020
Later among the works it cites.
Iitk-rsa at semeval-2020 task 5: Detecting counterfactuals
Anirudh Anil Ojha, Rohin Garg, Shashank Gupta, and Ashutosh Modi. 2020 · 2020
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Ethan at semeval-2020 task 5: Modelling causal reasoning in language using neuro-symbolic cloud computing
Len Yabloko. 2020 · 2020
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SemEval-2020 task 5: Counterfactual recognition
Xiaoyu Yang, Stephen Obadinma, Huasha Zhao, Qiong Zhang, Stan Matwin, and Xiaodan Zhu. 2020a · 2020
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SemEval-2020 Task 5: Counterfactual Recognition
Xiaoyu Yang, Stephen Obadinma, Huasha Zhao, Qiong Zhang, Stan Matwin, and Xiaodan Zhu. 2020b · 2020
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Debiasing pre-trained contextualised embeddings
Masahiro Kaneko and Danushka Bollegala. 2021 · 2021
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