13 citations · 19 across the 7 of their papers we have counts for
7 papers · 1 filter
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
Yangyi Chen, Hongcheng Gao, Ganqu Cui +4
Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mi…
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm
Lei Xu, Yangyi Chen, Ganqu Cui +2
Prompt-based learning paradigm bridges the gap between pre-training and fine-tuning, and works effectively under the few-shot setting. However, we find that this learning paradigm…
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer
Fanchao Qi, Yangyi Chen, Xurui Zhang +3
Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation…
Multi-granularity Textual Adversarial Attack with Behavior Cloning
Yangyi Chen, Jin Su, Wei Wei
Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have obvious…
Automatic Construction of Sememe Knowledge Bases via Dictionaries
Fanchao Qi, Yangyi Chen, Fengyu Wang +3
A sememe is defined as the minimum semantic unit in linguistics. Sememe knowledge bases (SKBs), which comprise words annotated with sememes, enable sememes to be applied to natural…
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger
Fanchao Qi, Mukai Li, Yangyi Chen +4
Backdoor attacks are a kind of insidious security threat against machine learning models. After being injected with a backdoor in training, the victim model will produce adversary-…