Papers by Manuel Senge
One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks (2022.emnlp-main)
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| Challenge: | Existing research on the efficiency of differentially-private stochastic gradient descent (DP-SGD) in NLP is inconclusive or even counter-intuitive. |
| Approach: | They propose to use differentially-private stochastic gradient descent (DP-SGD) to preserve privacy in NLP by using modern neural models based on BERT and XtremeDistil architectures to conduct extensive experiments. |
| Outcome: | The proposed models and training strategies provide the best trade-off between privacy and performance on different NLP tasks. |