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 demonstrate that domain-specific pre-training offers performance
improvements across all tasks. We release the benchmark to encourage future
research in this domain.

通过多种任务的评估，我们介绍了隐私政策语言理解评估（PLUE）基准，并收集了大量的隐私政策，以促进未来该领域的研究；同时，我们证明了领域特定的预训练对所有任务都有性能提升。