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Adversarial example generation methods in NLP rely on models like language models or sentence encoders to determine if potential adversarial examples are valid.
Is bert really robust? natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2019 · 1907
Earlier work this paper cites.
WordNet: a lexical database for english
George A Miller. 1995 · 1995
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation
Karimollah Hajian-Tilaki. 2013 · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
Skip-Thought vectors
Ryan Kiros, Yukun Zhu, Russ R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Earlier work this paper cites.
Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2017 · 2017
Earlier work this paper cites.
HotFlip: White-Box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2017 · 2017
Earlier work this paper cites.
Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh. 2017 · 2017
Earlier work this paper cites.
Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018 · 2018
Cited alongside, same era.
Audio adversarial examples: Targeted attacks on Speech-to-Text
Nicholas Carlini and David Wagner. 2018 · 2018
Cited alongside, same era.
Daniel Cer, Yinfei Yang, Sheng-Yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018 · 2018
Cited alongside, same era.
Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay. 2018 · 2018
Cited alongside, same era.
Combating adversarial misspellings with robust word recognition
Danish Pruthi, Bhuwan Dhingra, and Zachary C Lipton. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Generating natural language adversarial examples through probability weighted word saliency
Shuhuai Ren, Yihe Deng, Kun He, and Wanxiang Che. 2019 · 2019
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
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Adversarial examples on graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019 · 2019
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
Cited alongside, same era.
Adversarial examples for natural language classification problems
Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau, and Stefano Ermon. 2018 · 2018
Cited alongside, same era.
TextBugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2018 · 2018
Cited alongside, same era.
Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
Cited alongside, same era.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Cited alongside, same era.
Reevaluating adversarial examples in natural language
John X Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, and Yanjun Qi. 2020a
Cited in the paper.
TextAttack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP
John X Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020b
Cited in the paper.
Later among the works it cites.
PAWS-X: A cross-lingual adversarial dataset for paraphrase identification
Yinfei Yang, Yuan Zhang, Chris Tar, and Jason Baldridge. 2019 · 2019
Later among the works it cites.
BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 2019
Later among the works it cites.
BAE: BERT-based adversarial examples for text classification
Siddhant Garg and Goutham Ramakrishnan. 2020 · 2020
Closest in time.
Robust encodings: A framework for combating adversarial typos
Erik Jones, Robin Jia, Aditi Raghunathan, and Percy Liang. 2020 · 2020
Closest in time.
Elephant in the room: An evaluation framework for assessing adversarial examples in NLP
Ying Xu, Xu Zhong, Antonio Jose Jimeno Yepes, and Jey Han Lau. 2020 · 2020
Closest in time.