Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

27 Jul 2019 Di Jin Zhijing Jin Joey Tianyi Zhou Peter Szolovits

Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples... (read more)

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Methods used in the Paper


METHOD TYPE
Weight Decay
Regularization
Residual Connection
Skip Connections
Adam
Stochastic Optimization
Layer Normalization
Normalization
Softmax
Output Functions
Scaled Dot-Product Attention
Attention Mechanisms
Dropout
Regularization
GELU
Activation Functions
Multi-Head Attention
Attention Modules
Attention Dropout
Regularization
WordPiece
Subword Segmentation
Linear Warmup With Linear Decay
Learning Rate Schedules
Dense Connections
Feedforward Networks
BERT
Language Models