PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English

TL;DR

The paper introduces PLUE, a comprehensive benchmark for evaluating natural language understanding of privacy policies, and shows that domain-specific pre-training improves model performance across multiple tasks.

Contribution

It presents a new multi-task benchmark for privacy policy understanding and demonstrates the benefits of domain-specific pre-training for NLP models in this domain.

Findings

Domain-specific pre-training improves performance across tasks

PLUE benchmark covers diverse privacy policy understanding tasks

Large privacy policy corpus enables effective domain adaptation

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.