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Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price.
Cross-lingual Name Tagging and Linking for 282 Languages
Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji. 2017 · 1958
Earlier work this paper cites.
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
Earlier work this paper cites.
Learning Word Vectors for Sentiment Analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork and Aaron Roth. 2013 · 2013
Earlier work this paper cites.
A gold standard dependency corpus for English
Natalia Silveira, Timothy Dozat, Marie Catherine De Marneffe, Samuel R. Bowman, Miriam Connor, John Bauer, and Christopher D. Manning. 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.
Deep Learning with Differential Privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
The GUM corpus: creating multilayer resources in the classroom
Amir Zeldes. 2017 · 2017
Earlier work this paper cites.
Annotation Artifacts in Natural Language Inference Data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
Earlier work this paper cites.
Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
Cited alongside, same era.
Know What You Don’t Know: Unanswerable Questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019 · 2019
Cited alongside, same era.
Sequential Learning of Convolutional Features for Effective Text Classification
Avinash Madasu and Vijjini Anvesh Rao. 2019 · 2019
Cited alongside, same era.
Massively Multilingual Transfer for NER
Afshin Rahimi, Yuan Li, and Trevor Cohn. 2019 · 2019
Cited alongside, same era.
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models
Subhabrata Mukherjee and Ahmed Hassan Awadallah. 2020 · 2020
Later among the works it cites.
A Primer in BERTology: What We Know About How BERT Works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
Later among the works it cites.
When differential privacy meets NLP: The devil is in the detail
Ivan Habernal. 2021 · 2021
Closest in time.
Learning and Evaluating a Differentially Private Pre-trained Language Model
Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, and Yossi Matias. 2021 · 2021
Closest in time.
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework
Abhik Jana and Chris Biemann. 2021 · 2021
Closest in time.
Opacus: User-Friendly Differential Privacy Library in PyTorch
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Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2020
Cited alongside, same era.
Hierarchical Graph Network for Multi-hop Question Answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu. 2020 · 2020
Cited alongside, same era.
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, and Andrew Trask. 2020 · 2020
Cited alongside, same era.
Differentially Private Language Models Benefit from Public Pre-training
Gavin Kerrigan, Dylan Slack, and Jens Tuyls. 2020 · 2020
Cited alongside, same era.
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov. 2021 · 2021
Closest in time.
How reparametrization trick broke differentially-private text representation learning
Ivan Habernal. 2022 · 2022
Closest in time.
Privacy-Preserving Graph Convolutional Networks for Text Classification
Timour Igamberdiev and Ivan Habernal. 2022 · 2022
Closest in time.