Defending Pre-trained Language Models from Adversarial Word
Substitutions Without Performance Sacrifice

Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice

30 May 2021

Papers citing "Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice"

11 / 11 papers shown

Title
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks Yang Wang Chenghua Lin ELM 92 0 0 05 Jan 2025
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples Maximilian Mozes Pontus Stenetorp Bennett Kleinberg Lewis D. Griffin AAML 71 99 0 13 Apr 2020
Adversarial NLI: A New Benchmark for Natural Language Understanding Yixin Nie Adina Williams Emily Dinan Joey Tianyi Zhou Jason Weston Douwe Kiela 84 991 0 31 Oct 2019
SG-Net: Syntax-Guided Machine Reading Comprehension Zhuosheng Zhang Yuwei Wu Junru Zhou Sufeng Duan Hai Zhao Rui Wang 55 187 0 14 Aug 2019
Combating Adversarial Misspellings with Robust Word Recognition Danish Pruthi Bhuwan Dhingra Zachary Chase Lipton 82 302 0 27 May 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 92 2,018 0 08 Feb 2019
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks Jason W. Wei Kai Zou 66 1,931 0 31 Jan 2019
On Adversarial Examples for Character-Level Neural Machine Translation J. Ebrahimi Daniel Lowd Dejing Dou AAML 41 218 0 23 Jun 2018
Generating Natural Language Adversarial Examples M. Alzantot Yash Sharma Ahmed Elgohary Bo-Jhang Ho Mani B. Srivastava Kai-Wei Chang AAML 332 921 0 21 Apr 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 293 715 0 17 Apr 2018
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 74 1,050 0 06 Nov 2017