PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies

Abstract

Benchmarks for general language understanding have been rapidly developing in recent years of NLP research, particularly because of their utility in choosing strong-performing models for practical downstream applications. While benchmarks have been proposed in the legal language domain, virtually no such benchmarks exist for privacy policies despite their increasing importance in modern digital life. This could be explained by privacy policies falling under the legal language domain, but we find evidence to the contrary that motivates a separate benchmark for privacy policies. Consequently, we propose PrivacyGLUE as the first comprehensive benchmark of relevant and high-quality privacy tasks for measuring general language understanding in the privacy language domain. Furthermore, we release performances from multiple transformer language models and perform model–pair agreement analysis to detect tasks where models benefited from domain specialization. Our findings show the importance of in-domain pretraining for privacy policies. We believe PrivacyGLUE can accelerate NLP research and improve general language understanding for humans and AI algorithms in the privacy language domain, thus supporting the adoption and acceptance rates of solutions based on it.

Understanding Website Privacy Policies—A Longitudinal Analysis Using Natural Language Processing

“…Furthermore, the PrivacyGLUE [38] benchmark was proposed to address the lack of comprehensive benchmarks specifically designed for privacy policies. The benchmark includes the performance evaluations of transformer language models and emphasizes the importance of in-domain pre-training for privacy policies.…”