9 citations · 9 across the 3 of their papers we have counts for
7 papers
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Zongyi Li, Jianhan Xu, Jiehang Zeng +5
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversari…
SENT: Sentence-level Distant Relation Extraction via Negative Training
Ruotian Ma, Tao Gui, Linyang Li +3
Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications t…
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Jiehang Zeng, Xiaoqing Zheng, Jianhan Xu +3
Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions. However, all existing ce…
Generating Adversarial Examples in Chinese Texts Using Sentence-Pieces
Linyang Li, Yunfan Shao, Demin Song +2
Adversarial attacks in texts are mostly substitution-based methods that replace words or characters in the original texts to achieve success attacks. Recent methods use pre-trained…
TAVAT: Token-Aware Virtual Adversarial Training for Language Understanding
Linyang Li, Xipeng Qiu
Gradient-based adversarial training is widely used in improving the robustness of neural networks, while it cannot be easily adapted to natural language processing tasks since the…
BERT-ATTACK: Adversarial Attack Against BERT Using BERT
Linyang Li, Ruotian Ma, Qipeng Guo +2
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 advers…