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Question answering (QA) models for reading comprehension tend to learn shortcut solutions rather than the solutions intended by QA datasets.
Roberta: A robustly optimized bert pretraining approach
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Universal coding, information, prediction, and estimation
Rissanen, J. 1984 · 1984
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Flat Minima
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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Adversarial training for large neural language models
Liu, X.; Cheng, H.; He, P.; Chen, W.; Wang, Y.; Poon, H.; and Gao, J. 2020 · 2004
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Unbiased look at dataset bias
Torralba, A.; and Efros, A. A. 2011 · 2011
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
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RACE: Large-scale ReAding Comprehension Dataset From Examinations
Lai, G.; Xie, Q.; Liu, H.; Yang, Y.; and Hovy, E. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L. u.; and Polosukhin, I. 2017 · 2017
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Making Neural QA as Simple as Possible but not Simpler
Weissenborn, D.; Wiese, G.; and Seiffe, L. 2017 · 2017
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Semi-Supervised QA with Generative Domain-Adaptive Nets
Yang, Z.; Hu, J.; Salakhutdinov, R.; and Cohen, W. 2017 · 2017
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Annotation Artifacts in Natural Language Inference Data
Gururangan, S.; Swayamdipta, S.; Levy, O.; Schwartz, R.; Bowman, S.; and Smith, N. A. 2018 · 2018
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Visualizing the Loss Landscape of Neural Nets
Li, H.; Xu, Z.; Taylor, G.; Studer, C.; and Goldstein, T. 2018 · 2018
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What Makes Reading Comprehension Questions Easier?
Sugawara, S.; Inui, K.; Sekine, S.; and Aizawa, A. 2018 · 2018
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Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Clark, C.; Yatskar, M.; and Zettlemoyer, L. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Improving the Robustness of Question Answering Systems to Question Paraphrasing
Gan, W. C.; and Ng, H. T. 2019 · 2019
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Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA
Jiang, Y.; and Bansal, M. 2019 · 2019
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Natural Questions: A Benchmark for Question Answering Research
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets
Sugawara, S.; Stenetorp, P.; Inui, K.; and Aizawa, A. 2020 · 2020
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Towards Debiasing NLU Models from Unknown Biases
Utama, P. A.; Moosavi, N. S.; and Gurevych, I. 2020 · 2020
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Information-Theoretic Probing with Minimum Description Length
Voita, E.; and Titov, I. 2020 · 2020
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Improving QA Generalization by Concurrent Modeling of Multiple Biases
Wu, M.; Moosavi, N. S.; Rücklé, A.; and Gurevych, I. 2020 · 2020
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ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
Yu, W.; Jiang, Z.; Dong, Y.; and Feng, J. 2020 · 2020
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Competency Problems: On Finding and Removing Artifacts in Language Data
Gardner, M.; Merrill, W.; Dodge, J.; Peters, M.; Ross, A.; Singh, S.; and Smith, N. A. 2021 · 2021
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Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; Toutanova, K.; Jones, L.; Kelcey, M.; Chang, M.-W.; Dai, A. M.; Uszkoreit, J.; Le, Q.; and Petrov, S. 2019 · 2019
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McCoy, T.; Pavlick, E.; and Linzen, T. 2019 · 2019
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Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
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Evaluating Models’ Local Decision Boundaries via Contrast Sets
Gardner, M.; Artzi, Y.; Basmov, V.; Berant, J.; Bogin, B.; Chen, S.; Dasigi, P.; Dua, D.; Elazar, Y.; Gottumukkala, A.; Gupta, N.; Hajishirzi, H.; Ilharco, G.; Khashabi, D.; Lin, K.; Liu, J.; Liu, N. F.; Mulcaire, P.; Ning, Q.; Singh, S.; Smith, N. A.; Subramanian, S.; Tsarfaty, R.; Wallace, E.; Zhang, A.; and Zhou, B. 2020 · 2020
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Shortcut learning in deep neural networks
Geirhos, R.; Jacobsen, J.-H.; Michaelis, C.; Zemel, R.; Brendel, W.; Bethge, M.; and Wichmann, F. A. 2020 · 2020
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spaCy: Industrial-strength Natural Language Processing in Python
Honnibal, M.; Montani, I.; Van Landeghem, S.; and Boyd, A. 2020 · 2020
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Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
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Why Machine Reading Comprehension Models Learn Shortcuts?
Lai, Y.; Zhang, C.; Feng, Y.; Huang, Q.; and Zhao, D. 2021 · 2021
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Predicting Inductive Biases of Pre-Trained Models
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Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
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Info{BERT}: Improving Robustness of Language Models from An Information Theoretic Perspective
Wang, B.; Wang, S.; Cheng, Y.; Gan, Z.; Jia, R.; Li, B.; and Liu, J. 2021 · 2021
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Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
Scimeca, L.; Oh, S. J.; Chun, S.; Poli, M.; and Yun, S. 2022 · 2022
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Adversarial Examples for Evaluating Reading Comprehension Systems
Jia, R.; and Liang, P. 2017 · 2031
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