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- W4385571105 abstract "Privacy policies provide individuals with information about their rights and how their personal information is handled. Natural language understanding (NLU) technologies can support individuals and practitioners to understand better privacy practices described in lengthy and complex documents. However, existing efforts that use NLU technologies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. To this end, we introduce the Privacy Policy Language Understanding Evaluation (PLUE) benchmark, a multi-task benchmark for evaluating the privacy policy language understanding across various tasks. We also collect a large corpus of privacy policies to enable privacy policy domain-specific language model pre-training. We evaluate several generic pre-trained language models and continue pre-training them on the collected corpus. We demonstrate that domain-specific continual pre-training offers performance improvements across all tasks. The code and models are released at https://github.com/JFChi/PLUE." @default.
- W4385571105 created "2023-08-05" @default.
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- W4385571105 date "2023-01-01" @default.
- W4385571105 modified "2023-09-25" @default.
- W4385571105 title "PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English" @default.
- W4385571105 doi "https://doi.org/10.18653/v1/2023.acl-short.31" @default.
- W4385571105 hasPublicationYear "2023" @default.
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