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We develop a novel optimization method for NLPbackdoor inversion.
Roberta: A robustly optimized BERT pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 1907
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Sang, E. F. and De Meulder, F · 2003
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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Tjong Kim Sang, E. F. and De Meulder, F · 2003
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Bbn pronoun coreference and entity type corpus
Weischedel, R. and Brunstein, A · 2005
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Word representations: A simple and general method for semi-supervised learning
Turian, J., Ratinov, L.-A., and Bengio, Y · 2010
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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OntoNotes: The 90% solution
Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., and Weischedel, R · 2015
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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context2vec: Learning generic context embedding with bidirectional lstm
Melamud, O., Goldberger, J., and Dagan, I · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X., Liu, C., Li, B., Lu, K., and Song, D · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., and Zhang, X · 2017
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Semi-supervised sequence tagging with bidirectional language models
Peters, M. E., Ammar, W., Bhagavatula, C., and Power, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Generating natural language adversarial examples
Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.-J., Srivastava, M., and Chang, K.-W · 2018
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Allennlp: A deep semantic natural language processing platform
Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N., Peters, M., Schmitz, M., and Zettlemoyer, L · 2018
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Fine-pruning: Defending against backdooring attacks on deep neural networks
Liu, K., Dolan-Gavitt, B., and Garg, S · 2018
Deepset roberta
deepset · 2020
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Trojai competition
IARPA · 2020
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Weight poisoning attacks on pre-trained models
Kurita, K., Michel, P., and Neubig, G · 2020
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Reflection backdoor: A natural backdoor attack on deep neural networks
Liu, Y., Ma, X., Bailey, J., and Lu, F · 2020
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Onion: A simple and effective defense against textual backdoor attacks
Qi, F., Chen, Y., Li, M., Yao, Y., Liu, Z., and Sun, M · 2020
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Cited alongside, same era.
A backdoor attack against lstm-based text classification systems
Dai, J., Chen, C., and Li, Y · 2019
Cited alongside, same era.
Abs: Scanning neural networks for back-doors by artificial brain stimulation
Liu, Y., Lee, W.-C., Tao, G., Ma, S., Aafer, Y., and Zhang, X · 2019
Cited alongside, same era.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Ni, J., Li, J., and McAuley, J · 2019
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Combating adversarial misspellings with robust word recognition
Pruthi, D., Dhingra, B., and Lipton, Z. C · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2019
Cited alongside, same era.
Universal adversarial triggers for attacking and analyzing nlp
Wallace, E., Feng, S., Kandpal, N., Gardner, M., and Singh, S · 2019
Cited alongside, same era.
Salem, A., Wen, R., Backes, M., Ma, S., and Zhang, Y · 2020
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Mobilebert: a compact task-agnostic bert for resource-limited devices
Sun, Z., Yu, H., Song, X., Liu, R., Yang, Y., and Zhou, D · 2020
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Practical detection of trojan neural networks: Data-limited and data-free cases
Wang, R., Zhang, G., Liu, S., Chen, P.-Y., Xiong, J., and Wang, M · 2020
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T-miner: A generative approach to defend against trojan attacks on dnn-based text classification
Azizi, A., Tahmid, I. A., Waheed, A., Mangaokar, N., Pu, J., Javed, M., Reddy, C. K., and Viswanath, B · 2021
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Mitigating backdoor attacks in lstm-based text classification systems by backdoor keyword identification
Chen, C. and Dai, J · 2021
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Badnl: Backdoor attacks against nlp models with semantic-preserving improvements
Chen, X., Salem, A., Chen, D., Backes, M., Ma, S., Shen, Q., Wu, Z., and Zhang, Y · 2021
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Towards robustness against natural language word substitutions
Dong, X., Luu, A. T., Ji, R., and Liu, H · 2021
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Text backdoor detection using an interpretable rnn abstract model
Fan, M., Si, Z., Xie, X., Liu, Y., and Liu, T · 2021
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Adversarial neuron pruning purifies backdoored deep models
Wu, D. and Wang, Y · 2021
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Red alarm for pre-trained models: Universal vulnerabilities by neuron-level backdoor attacks
Zhang, Z., Xiao, G., Li, Y., Lv, T., Qi, F., Liu, Z., Wang, Y., Jiang, X., and Sun, M · 2021
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Model orthogonalization: Class distance hardening in neural networks for better security
Tao, G., Liu, Y., Shen, G., Xu, Q., An, S., Zhang, Z., and Zhang, X · 2022
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