Fetching the paper…
Reading the bibliography…
In attempts to produce ML models less reliant on spurious patterns in NLP datasets, researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in-the-loop process in which given some documents and their (initial) labels, humans must revise the text to make a counterfactual label applicable.
The method of path coefficients
Sewall Wright · 1934
Earlier work this paper cites.
A statistical interpretation of term specificity and its application in retrieval
Karen Sparck Jones · 1972
Earlier work this paper cites.
Bayesian netwcrks: A model cf self-activated memory for evidential reasoning
Judea Pearl · 1985
Earlier work this paper cites.
What constitutes fairness in work settings? a four-component model of procedural justice
Steven L Blader and Tom R Tyler · 2003
Earlier work this paper cites.
Framewise phoneme classification with bidirectional lstm and other neural network architectures
Alex Graves and Jürgen Schmidhuber · 2005
Earlier work this paper cites.
Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko · 2007
Earlier work this paper cites.
Modeling annotators: A generative approach to learning from annotator rationales
Omar F Zaidan and Jason Eisner · 2008
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
On causal and anticausal learning
B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, and J Mooij · 2012
Earlier work this paper cites.
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Masashi Sugiyama and Motoaki Kawanabe · 2012
Earlier work this paper cites.
Intriguing Properties of Neural Networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning · 2016
Earlier work this paper cites.
Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky · 2016
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
Earlier work this paper cites.
Learning causal structures using regression invariance
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, and Kun Zhang · 2017
Earlier work this paper cites.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing · 2017
Earlier work this paper cites.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
Earlier work this paper cites.
Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
Earlier work this paper cites.
A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio · 2017
Cited alongside, same era.
Learning with feature feedback: from theory to practice
Stefanos Poulis and Sanjoy Dasgupta · 2017
Cited alongside, same era.
Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
Cited alongside, same era.
Amazon scraps secret ai recruiting tool that showed bias against women
Jeffrey Dastin · 2018
Cited alongside, same era.
Measuring and mitigating unintended bias in text classification
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2018
Cited alongside, same era.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2018
Later among the works it cites.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Later among the works it cites.
Queens are powerful too: Mitigating gender bias in dialogue generation
Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, and Jason Weston · 2019
Later among the works it cites.
It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution
Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
Cited alongside, same era.
Breaking nli systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg · 2018
Cited alongside, same era.
Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning
Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P Gummadi, and Adrian Weller · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A Smith · 2018
Cited alongside, same era.
Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer · 2018
Cited alongside, same era.
How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C Lipton · 2018
Cited alongside, same era.
Examining gender and race bias in two hundred sentiment analysis systems
Svetlana Kiritchenko and Saif Mohammad · 2018
Cited alongside, same era.
Later among the works it cites.
On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel Bowman, and Rachel Rudinger · 2019
Later among the works it cites.
Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan · 2019
Later among the works it cites.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Later among the works it cites.
What do deep networks like to read?
Jonas Pfeiffer, Aishwarya Kamath, Iryna Gurevych, and Sebastian Ruder · 2019
Later among the works it cites.
Transforming delete, retrieve, generate approach for controlled text style transfer
Akhilesh Sudhakar, Bhargav Upadhyay, and Arjun Maheswaran · 2019
Later among the works it cites.
Universal adversarial triggers for nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
Later among the works it cites.
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, et al · 2019
Later among the works it cites.
Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology
Ran Zmigrod, Sebastian J. Mielke, Hanna Wallach, and Ryan Cotterell · 2019
Later among the works it cites.
Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan · 2020
Closest in time.
Switching regression models and causal inference in the presence of discrete latent variables
Rune Christiansen and Jonas Peters · 2020
Closest in time.
ERASER: A benchmark to evaluate rationalized NLP models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace · 2020
Closest in time.
Can autonomous vehicles identify, recover from, and adapt to distribution shifts?
Angelos Filos, P. Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, and Yarin Gal · 2020
Closest in time.
Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton · 2020
Closest in time.
Politeness transfer: A tag and generate approach
Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, and Shrimai Prabhumoye · 2020
Closest in time.
Robustness to spurious correlations via human annotations
Megha Srivastava, Tatsunori Hashimoto, and Percy Liang · 2020
Closest in time.
Learning what makes a difference from counterfactual examples and gradient supervision
Damien Teney, Ehsan Abbasnedjad, and Anton van den Hengel · 2020
Closest in time.
Distributional robustness as a guiding principle for causality in cognitive neuroscience
Sebastian Weichwald and Jonas Peters · 2020
Closest in time.