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Robustness

529 directly classified papers

Papers per year

Papers

RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining ACL 2022
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation ACL 2022
An Investigation of the (In)effectiveness of Counterfactually Augmented Data ACL 2022
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model ACL 2022
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples ACL 2022
Sibylvariant Transformations for Robust Text Classification ACL 2022
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings ACL 2022
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting ACL 2022
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness ACL 2022
ASSIST: Towards Label Noise-Robust Dialogue State Tracking ACL 2022
Improving the Adversarial Robustness of NLP Models by Information Bottleneck ACL 2022
Interpreting the Robustness of Neural NLP Models to Textual Perturbations ACL 2022
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification ACL 2022
Towards Stronger Adversarial Baselines Through Human-AI Collaboration ACL 2022
Language Invariant Properties in Natural Language Processing ACL 2022
Enhancing Adversarial Training With Second-Order Statistics of Weights CVPR 2022
The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization CVPR 2022
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation CVPR 2022
Does Robustness on ImageNet Transfer to Downstream Tasks? CVPR 2022
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models EMNLP 2022
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models EMNLP 2022
Textual Manifold-based Defense Against Natural Language Adversarial Examples EMNLP 2022
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models EMNLP 2022
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP EMNLP 2022
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals EMNLP 2022