BERT-ATTACK: Adversarial Attack Against BERT Using BERT

Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods. Current successful attack methods for texts usually adopt heuristic replacement strategies on the character or word level, which remains challenging to find the optimal solution in the massive space of possible combinations of replacements while preserving semantic consistency and language fluency... (read more)

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Methods used in the Paper


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