PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English
This work addresses the problem of understanding complex privacy policies for individuals and practitioners, but it is incremental as it builds on existing language models and benchmarks.
The authors tackled the lack of a comprehensive benchmark for natural language understanding in privacy policies by introducing PLUE, a multi-task evaluation benchmark, and showed that domain-specific continual pre-training improves performance across all tasks, with specific gains such as up to 5% accuracy improvements in some tasks.
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.