DP-NMT: Scalable Differentially-Private Machine Translation
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866907879500414976 |
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| author | Igamberdiev, Timour Vu, Doan Nam Long Künnecke, Felix Yu, Zhuo Holmer, Jannik Habernal, Ivan |
| author_facet | Igamberdiev, Timour Vu, Doan Nam Long Künnecke, Felix Yu, Zhuo Holmer, Jannik Habernal, Ivan |
| contents | Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_14465 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DP-NMT: Scalable Differentially-Private Machine Translation Igamberdiev, Timour Vu, Doan Nam Long Künnecke, Felix Yu, Zhuo Holmer, Jannik Habernal, Ivan Computation and Language Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community. |
| title | DP-NMT: Scalable Differentially-Private Machine Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.14465 |