Shankar, A.; Waldis, A.; Bless, C.; Andueza Rodriguez, M.; Mazzola, L. PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies. Appl. Sci.2023, 13, 3701.
Shankar, A.; Waldis, A.; Bless, C.; Andueza Rodriguez, M.; Mazzola, L. PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies. Appl. Sci. 2023, 13, 3701.
Shankar, A.; Waldis, A.; Bless, C.; Andueza Rodriguez, M.; Mazzola, L. PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies. Appl. Sci.2023, 13, 3701.
Shankar, A.; Waldis, A.; Bless, C.; Andueza Rodriguez, M.; Mazzola, L. PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies. Appl. Sci. 2023, 13, 3701.
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.
Keywords
Privacy Policies; NLP; benchmark; general language understanding; domain specialization and generalization
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
