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Natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance on specific datasets.
End-to-End Self-Debiasing Framework for Robust NLU Training
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Analyzing the Behavior of Visual Question Answering Models
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Dimensionality Reduction by Learning an Invariant Mapping
Hadsell, R.; Chopra, S.; and LeCun, Y. 2006 · 2006
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A Large Annotated Corpus for Learning Natural Language Inference
Bowman, S. R.; Angeli, G.; Potts, C.; and Manning, C. D. 2015 · 2015
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Evaluating Compositionality in Sentence Embeddings
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Breaking NLI Systems with Sentences that Require Simple Lexical Inferences
Glockner, M.; Shwartz, V.; and Goldberg, Y. 2018 · 2018
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Annotation Artifacts in Natural Language Inference Data
Gururangan, S.; Swayamdipta, S.; Levy, O.; Schwartz, R.; Bowman, S. R.; and Smith, N. A. 2018 · 2018
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How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks
Kaushik, D.; and Lipton, Z. C. 2018 · 2018
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Stress Test Evaluation for Natural Language Inference
Naik, A.; Ravichander, A.; Sadeh, N. M.; Rosé, C. P.; and Neubig, G. 2018 · 2018
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FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J.; Vlachos, A.; Christodoulopoulos, C.; and Mittal, A. 2018 · 2018
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A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A.; Nangia, N.; and Bowman, S. R. 2018 · 2018
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Unsupervised Feature Learning via Non-Parametric Instance Discrimination
Wu, Z.; Xiong, Y.; Yu, S. X.; and Lin, D. 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.; Lee, K.; and Toutanova, K. 2019 · 2019
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Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual
He, H.; Zha, S.; and Wang, H. 2019 · 2019
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Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
McCoy, T.; Pavlick, E.; and Linzen, T. 2019 · 2019
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Towards Debiasing Fact Verification Models
Schuster, T.; Shah, D. J.; Yeo, Y. J. S.; Filizzola, D.; Santus, E.; and Barzilay, R. 2019 · 2019
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HellaSwag: Can a Machine Really Finish Your Sentence?
Zellers, R.; Holtzman, A.; Bisk, Y.; Farhadi, A.; and Choi, Y. 2019 · 2019
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PAWS: Paraphrase Adversaries from Word Scrambling
Zhang, Y.; Baldridge, J.; and He, L. 2019 · 2019
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Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
Bender, E. M.; and Koller, A. 2020 · 2020
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A Simple Framework for Contrastive Learning of Visual Representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. E. 2020 · 2020
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
Du, M.; Manjunatha, V.; Jain, R.; Deshpande, R.; Dernoncourt, F.; Gu, J.; Sun, T.; and Hu, X. 2021 · 2021
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SimCSE: Simple Contrastive Learning of Sentence Embeddings
Gao, T.; Yao, X.; and Chen, D. 2021 · 2021
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DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
Giorgi, J. M.; Nitski, O.; Wang, B.; and Bader, G. D. 2021 · 2021
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Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
Gunel, B.; Du, J.; Conneau, A.; and Stoyanov, V. 2021 · 2021
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KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier
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Shortcut Learning in Deep Neural Networks
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Momentum Contrast for Unsupervised Visual Representation Learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. B. 2020 · 2020
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Learning the Difference that Makes a Difference With Counterfactually-Augmented Data
Kaushik, D.; Hovy, E. H.; and Lipton, Z. C. 2020 · 2020
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Supervised Contrastive Learning
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End-to-End Bias Mitigation by Modelling Biases in Corpora
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On the Importance of Pre-training Data Volume for Compact Language Models
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Debiasing Methods in Natural Language Understanding Make Bias More Accessible
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WinoGrande: An Adversarial Winograd Schema Challenge at Scale
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Learning from Others’ Mistakes: Avoiding Dataset Biases without Modeling Them
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Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
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Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
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CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding
Wang, D.; Ding, N.; Li, P.; and Zheng, H. 2021 · 2021
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Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey
Du, M.; He, F.; Zou, N.; Tao, D.; and Hu, X. 2022 · 2022
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Tailor: Generating and Perturbing Text with Semantic Controls
Ross, A.; Wu, T.; Peng, H.; Peters, M. E.; and Gardner, M. 2022 · 2022
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Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
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Adversarial Examples for Evaluating Reading Comprehension Systems
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