Website privacy policies represent the single most important source of
information for users to gauge how their personal data are collected, used and
shared by companies. However, privacy policies are often vague and people
struggle to understand the content. Their opaqueness poses a significant
challenge to both users and policy regulators. In this paper, we seek to
identify vague content in privacy policies. We construct the first corpus of
human-annotated vague words and sentences and present empirical studies on
automatic vagueness detection. In particular, we investigate context-aware and
context-agnostic models for predicting vague words, and explore
auxiliary-classifier generative adversarial networks for characterizing
sentence vagueness. Our experimental results demonstrate the effectiveness of
proposed approaches. Finally, we provide suggestions for resolving vagueness
and improving the usability of privacy policies.

该论文旨在使用构建的第一个含人类标注的模糊词汇和句子的语料库，对隐私政策的模糊内容进行自动检测，研究上下文敏感和上下文不敏感模型以及辅助分类生成性对抗网络来表征句子模糊性，研究结果表明所提出的方法有效，并提出解决模糊性和提高隐私政策可用性的建议。