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Many NLP datasets have been found to contain shortcuts: simple decision rules that achieve surprisingly high accuracy.
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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Combining feature and instance attribution to detect artifacts
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A value for n-person games
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Syntax-directed transduction
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The estimation of stochastic context-free grammars using the inside-outside algorithm
Karim Lari and Steve J Young. 1990 · 1990
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Synchronous tree-adjoining grammars
Stuart M Shieber and Yves Schabes. 1990 · 1990
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Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Dekai Wu. 1997 · 1997
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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Quasi-synchronous grammars: Alignment by soft projection of syntactic dependencies
David A Smith and Jason Eisner. 2006 · 2006
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What is the Jeopardy model? A quasi-synchronous grammar for QA
Mengqiu Wang, Noah A Smith, and Teruko Mitamura. 2007 · 2007
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Bayesian synchronous grammar induction
Phil Blunsom, Trevor Cohn, and Miles Osborne. 2008 · 2008
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Unsupervised word segmentation for Sesotho using adaptor grammars
Mark Johnson. 2008 · 2008
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Modeling perspective using adaptor grammars
Eric Hardisty, Jordan Boyd-Graber, and Philip Resnik. 2010 · 2010
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Bayesian synchronous tree-substitution grammar induction and its application to sentence compression
Elif Yamangil and Stuart M Shieber. 2010 · 2010
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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011 · 2011
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Exploring adaptor grammars for native language identification
Sze-Meng Jojo Wong, Mark Dras, and Mark Johnson. 2012 · 2012
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A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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“Why should I trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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First Quora dataset release: Question pairs
Shankar Iyer, Nikhil Dandekar, Kornél Csernai, et al. 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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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Understanding global feature contributions with additive importance measures
Ian Covert, Scott M Lundberg, and Su-In Lee. 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, et al. 2020 · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 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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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020
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Finding variants for construction-based dialectometry: A corpus-based approach to regional CxGs
Jonathan Dunn. 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
Cited alongside, same era.
Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
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Compound probabilistic context-free grammars for grammar induction
Yoon Kim, Chris Dyer, and Alexander M Rush. 2019 · 2019
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Exposing shallow heuristics of relation extraction models with challenge data
Shachar Rosenman, Alon Jacovi, and Yoav Goldberg. 2020 · 2020
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Torch-Struct: Deep structured prediction library
Alexander M Rush. 2020 · 2020
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Identifying spurious correlations for robust text classification
Zhao Wang and Aron Culotta. 2020 · 2020
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Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, and Katja Filippova. 2021 · 2021
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Competency problems: On finding and removing artifacts in language data
Matt Gardner, William Merrill, Jesse Dodge, Matthew E Peters, Alexis Ross, Sameer Singh, and Noah A Smith. 2021 · 2021
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Sequence-to-sequence learning with latent neural grammars
Yoon Kim. 2021 · 2021
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Just Train Twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn. 2021 · 2021
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Explaining NLP models via minimal contrastive editing (MiCE)
Alexis Ross, Ana Marasović, and Matthew E Peters. 2021 · 2021
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Informativeness and invariance: Two perspectives on spurious correlations in natural language
Jacob Eisenstein. 2022 · 2022
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Identifying and mitigating spurious correlations for improving robustness in NLP models
Tianlu Wang, Diyi Yang, and Xuezhi Wang. 2022 · 2022
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Summarizing differences between text distributions with natural language
Ruiqi Zhong, Charlie Snell, Dan Klein, and Jacob Steinhardt. 2022 · 2022
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