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Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance on out-of-distribution ones.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019 · 1907
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A kernel statistical test of independence
Arthur Gretton, Kenji Fukumizu, Choon Teo, Le Song, Bernhard Schölkopf, and Alex Smola. 2007 · 2007
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Random features for large-scale kernel machines
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Kernels for vector-valued functions: A review
Mauricio A Alvarez, Lorenzo Rosasco, Neil D Lawrence, et al. 2012 · 2012
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Learning from others’ mistakes: Avoiding dataset biases without modeling them
Victor Sanh, Thomas Wolf, Yonatan Belinkov, and Alexander M Rush. 2020 · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
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Reparameterization gradients through acceptance-rejection sampling algorithms
Christian Naesseth, Francisco Ruiz, Scott Linderman, and David Blei. 2017 · 2017
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High-order covariate interacted lasso for feature selection
Zhihong Zhang, Yiyang Tian, Lu Bai, Jianbing Xiahou, and Edwin Hancock. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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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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Deep learning: A critical appraisal
Gary Marcus. 2018 · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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The fact extraction and verification (fever) shared task
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 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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Learning de-biased representations with biased representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, and Seong Joon Oh. 2020 · 2020
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Climbing towards nlu: On meaning, form, and understanding in the age of data
Emily M Bender and Alexander Koller. 2020 · 2020
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Learning to model and ignore dataset bias with mixed capacity ensembles
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2020 · 2020
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Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C Wallace, and Yulia Tsvetkov. 2020 · 2020
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Stable learning via sample reweighting
Zheyan Shen, Peng Cui, Tong Zhang, and Kun Kunag. 2020 · 2020
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Towards debiasing nlu models from unknown biases
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020b · 2020
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Deep networks with adaptive nyström approximation
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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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Analyzing compositionality-sensitivity of nli models
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Towards debiasing fact verification models
Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, and Regina Barzilay. 2019 · 2019
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Mind the trade-off: Debiasing nlu models without degrading the in-distribution performance
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020a
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Infobert: Improving robustness of language models from an information theoretic perspective
Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, and Jingjing Liu. 2020 · 2020
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Debiased visual question answering from feature and sample perspectives
Zhiquan Wen, Guanghui Xu, Mingkui Tan, Qingyao Wu, and Qi Wu. 2021 · 2021
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Uncertainty calibration for ensemble-based debiasing methods
Ruibin Xiong, Yimeng Chen, Liang Pang, Xueqi Cheng, Zhi-Ming Ma, and Yanyan Lan. 2021 · 2021
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Deep stable learning for out-of-distribution generalization
Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, and Zheyan Shen. 2021 · 2021
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