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Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerabilities.
Wordnet: a lexical database for english
G.A. Miller · 1995
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
B. Pang and L. Lee · 2005
Earlier work this paper cites.
Hownet and the computation of meaning. World Scientific, 2006
Z. Dong and Q. Dong · 2006
Earlier work this paper cites.
Learning word vectors for sentiment analysis
A. Maas, R. Daly, T. Peter, D. Huang, Y. Andrew, and C. Potts · 2011
Earlier work this paper cites.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. Manning · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
S. Bowman, G. Angeli, C. Potts, and C. Manning · 2015
Earlier work this paper cites.
Supervised learning of universal sentence representations from natural language inference data
A. Conneau, D. Kiela, H. Schwenk, L. Barrault, and A. Bordes · 2017
Earlier work this paper cites.
Generating natural language adversarial examples
M. Alzantot, Y. Sharma, A. Elgohary, B. Ho, M. Srivastava, and K. Chang · 2018
Earlier work this paper cites.
On adversarial examples for character-level neural machine translation
J. Ebrahimi, D. Lowd, and D. Dou · 2018
Earlier work this paper cites.
Evaluating and enhancing the robustness of dialogue systems: A case study on a negotiation agent
M. Cheng, W. Wei, and C. Hsieh · 2019
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
Combating adversarial misspellings with robust word recognition
D. Pruthi, B. Dhingra, and Z. Lipton · 2019
Cited alongside, same era.
Generating natural language adversarial examples through probability weighted word saliency
S. Ren, Y. Deng, K. He, and W. Che · 2019
Cited alongside, same era.
Learning to discriminate perturbations for blocking adversarial attacks in text classification
Y. Zhou, J. Jiang, K. Chang, and W. Wang · 2019
Cited alongside, same era.
Is BERT really robust? A strong baseline for natural language attack on text classification and entailment
D. Jin, Z. Jin, J. Zhou, and P. Szolovits · 2020
Cited alongside, same era.
BERT-ATTACK: adversarial attack against BERT using BERT
Making pre-trained language models better few-shot learners
T. Gao, A. Fisch, and D. Chen · 2021
Later among the works it cites.
Dialogue state tracking with a language model using schema-driven prompting
C. Lee, H. Cheng, and M. Ostendorf · 2021
Later among the works it cites.
Prefix-tuning: Optimizing continuous prompts for generation
X. Li and P. Liang · 2021
Later among the works it cites.
Using adversarial attacks to reveal the statistical bias in machine reading comprehension models
J. Lin, J. Zou, and N. Ding · 2021
Later among the works it cites.
P. Liu, W. Yuan, J. Fu, Z.Jiang, H. Hayashi, and G. Neubig · 2021
Later among the works it cites.
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L. Li, R. Ma, Q. Guo, X. Xue, and X. Qiu · 2020
Cited alongside, same era.
T3: tree-autoencoder constrained adversarial text generation for targeted attack
B. Wangand H. Pei, B. Pan, Q. Chen, S. Wang, and B. Li · 2020
Cited alongside, same era.
Word-level textual adversarial attacking as combinatorial optimization
Y. Zang, F. Qi, C. Yang, Z. Liu, M. Zhang, Q. Liu, and M. Sun · 2020
Cited alongside, same era.
Evaluating and enhancing the robustness of neural network-based dependency parsing models with adversarial examples
X. Zheng, J. Zeng, Y. Zhou, C. Hsieh, M. Cheng, and Huang X · 2020
Cited alongside, same era.
Gsum: A general framework for guided neural abstractive summarization
Z. Dou, P. Liu, H. Hayashi, Z. Jiang, and G. Neubig · 2021
Cited alongside, same era.
Frequency-guided word substitutions for detecting textual adversarial examples
M. Mozes, P. Stenetorp, B. Kleinberg, and L. Griffin · 2021
Later among the works it cites.
Exploiting cloze-questions for few-shot text classification and natural language inference
T. Schick and H. Schütze · 2021
Later among the works it cites.
Adversarial training with fast gradient projection method against synonym substitution based text attacks
X. Wang, Y. Yang, Y. Deng, and K. He · 2021
Later among the works it cites.
Crafting adversarial examples for neural machine translation
X. Zhang, J. Zhang, Z. Chen, and K. He · 2021
Later among the works it cites.