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Pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks.
E. T. K. Sang, “Introduction to the conll-2002 shared task: Language-independent named entity recognition,” in Proceedings of CoNLL-2002 . Unknown Publisher, 2002, pp. 155–158
2002
Earlier work this paper cites.
H. Erdogan, “Sequence labeling: Generative and discriminative approaches,” in Proc. 9th Int. Conf. Mach. Learn. Appl. , 2010, pp. 1–132
2010
Earlier work this paper cites.
2013
Earlier work this paper cites.
Y. Zhu, R. Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba, and S. Fidler, “Aligning books and movies: Towards story-like visual explanations by watching movies and reading books,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 19–27
2015
Earlier work this paper cites.
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “SQuAD: 100,000+ questions for machine comprehension of text,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Austin, Texas: Association for Computational Linguistics, Nov. 2016, pp. 2383–2392. [Online]. Available: https://aclanthology.org/D16-1264
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman, “GLUE: A multi-task benchmark and analysis platform for natural language understanding,” in Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP . Brussels, Belgium: Association for Computational Linguistics, Nov. 2018, pp. 353–355. [Online]. Available: https://aclanthology.org/W18-5446
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Cited alongside, same era.
J. Dai, C. Chen, and Y. Li, “A backdoor attack against lstm-based text classification systems,” IEEE Access , vol. 7, pp. 138 872–138 878, 2019
2019
Cited alongside, same era.
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,” in 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 2019, pp. 707–723
2019
Cited alongside, same era.
2020
Later among the works it cites.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush, “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Online: Association for Computational Linguistics, Oct. 2020, pp. 38–45. [Online]. Available: https://www.aclweb.org/anthology/2020.emnlp-demos.6
2020
Later among the works it cites.
2021
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Closest in time.
2021
Closest in time.
F. Qi, Y. Chen, M. Li, Y. Yao, Z. Liu, and M. Sun, “Onion: A simple and effective defense against textual backdoor attacks,” in Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2021
2021
Closest in time.
2021
Closest in time.
HuggingFace, “Huggingface,” https://huggingface.co/models, accessed: 2021-10-01
2021
Closest in time.