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Towards Robustness Against Natural Language Word Substitutions

Towards Robustness Against Natural Language Word Substitutions

International Conference on Learning Representations (ICLR), 2021
28 July 2021
Xinshuai Dong
Anh Tuan Luu
Rongrong Ji
Hong Liu
    SILMAAML
ArXiv (abs)PDFHTML

Papers citing "Towards Robustness Against Natural Language Word Substitutions"

20 / 70 papers shown
Robust Textual Embedding against Word-level Adversarial Attacks
Robust Textual Embedding against Word-level Adversarial AttacksConference on Uncertainty in Artificial Intelligence (UAI), 2022
Yichen Yang
Xiaosen Wang
Kun He
AAML
188
21
0
28 Feb 2022
Constrained Optimization with Dynamic Bound-scaling for Effective
  NLPBackdoor Defense
Constrained Optimization with Dynamic Bound-scaling for Effective NLPBackdoor DefenseInternational Conference on Machine Learning (ICML), 2022
Guangyu Shen
Yingqi Liu
Guanhong Tao
Qiuling Xu
Zhuo Zhang
Shengwei An
Shiqing Ma
Xinming Zhang
AAML
224
52
0
11 Feb 2022
Identifying Adversarial Attacks on Text Classifiers
Identifying Adversarial Attacks on Text Classifiers
Zhouhang Xie
Jonathan Brophy
Adam Noack
Wencong You
Kalyani Asthana
Carter Perkins
Sabrina Reis
Sameer Singh
Daniel Lowd
AAML
149
11
0
21 Jan 2022
Robust Natural Language Processing: Recent Advances, Challenges, and
  Future Directions
Robust Natural Language Processing: Recent Advances, Challenges, and Future DirectionsIEEE Access (IEEE Access), 2022
Marwan Omar
Soohyeon Choi
Daehun Nyang
David A. Mohaisen
238
75
0
03 Jan 2022
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial
  Robustness?
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?Neural Information Processing Systems (NeurIPS), 2021
Xinhsuai Dong
Anh Tuan Luu
Min Lin
Shuicheng Yan
Hanwang Zhang
SILMAAML
172
74
0
22 Dec 2021
The King is Naked: on the Notion of Robustness for Natural Language
  Processing
The King is Naked: on the Notion of Robustness for Natural Language Processing
Emanuele La Malfa
Marta Z. Kwiatkowska
305
31
0
13 Dec 2021
Detecting Textual Adversarial Examples through Randomized Substitution
  and Vote
Detecting Textual Adversarial Examples through Randomized Substitution and Vote
Xiaosen Wang
Yifeng Xiong
Kun He
AAML
206
15
0
13 Sep 2021
TREATED:Towards Universal Defense against Textual Adversarial Attacks
TREATED:Towards Universal Defense against Textual Adversarial Attacks
Bin Zhu
Zhaoquan Gu
Le Wang
Zhihong Tian
AAML
101
8
0
13 Sep 2021
Towards Improving Adversarial Training of NLP Models
Towards Improving Adversarial Training of NLP ModelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Jin Yong Yoo
Yanjun Qi
AAML
488
145
0
01 Sep 2021
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language
  Understanding
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding
Lingyun Feng
Jianwei Yu
Deng Cai
Songxiang Liu
Haitao Zheng
Yan Wang
ELM
310
17
0
30 Aug 2021
Searching for an Effective Defender: Benchmarking Defense against
  Adversarial Word Substitution
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word SubstitutionConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Zongyi Li
Jianhan Xu
Jiehang Zeng
Linyang Li
Xiaoqing Zheng
Tao Gui
Kai-Wei Chang
Cho-Jui Hsieh
AAML
184
87
0
29 Aug 2021
Evaluating the Robustness of Neural Language Models to Input
  Perturbations
Evaluating the Robustness of Neural Language Models to Input PerturbationsConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
M. Moradi
Matthias Samwald
AAML
233
130
0
27 Aug 2021
On The State of Data In Computer Vision: Human Annotations Remain
  Indispensable for Developing Deep Learning Models
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
Z. Emam
Andrew Kondrich
Sasha Harrison
Felix Lau
Yushi Wang
Aerin Kim
E. Branson
VLM
138
17
0
31 Jul 2021
Self-Supervised Contrastive Learning with Adversarial Perturbations for
  Defending Word Substitution-based Attacks
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks
Zhao Meng
Yihan Dong
Mrinmaya Sachan
Roger Wattenhofer
AAML
161
11
0
15 Jul 2021
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]International Conference on Computational Logic (ICCL), 2021
Jiehang Zeng
Xiaoqing Zheng
Jianhan Xu
Linyang Li
Liping Yuan
Xuanjing Huang
AAML
327
91
0
08 May 2021
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing
  Attack
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing AttackEuropean Conference on Computer Vision (ECCV), 2021
Yixu Wang
Jie Li
Hong Liu
Yan Wang
Yongjian Wu
Feiyue Huang
Rongrong Ji
AAML
358
40
0
03 May 2021
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust TrainingConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Kuan-Hao Huang
Wasi Uddin Ahmad
Nanyun Peng
Kai-Wei Chang
AAML
310
38
0
17 Apr 2021
Certified Robustness to Programmable Transformations in LSTMs
Certified Robustness to Programmable Transformations in LSTMsConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Yuhao Zhang
Aws Albarghouthi
Loris Dántoni
AAML
170
24
0
15 Feb 2021
SHIELD: Defending Textual Neural Networks against Multiple Black-Box
  Adversarial Attacks with Stochastic Multi-Expert Patcher
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert PatcherAnnual Meeting of the Association for Computational Linguistics (ACL), 2020
Thai Le
Noseong Park
Dongwon Lee
AAML
169
25
0
17 Nov 2020
Generating Natural Adversarial Examples
Generating Natural Adversarial Examples
Zhengli Zhao
Dheeru Dua
Sameer Singh
GANAAML
576
643
0
31 Oct 2017
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