Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2018 | EMNLP | Conference |
Volume | Citations | PageRank |
abs/1808.06219 | 3 | 0.38 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Logan Lebanoff | 1 | 8 | 2.12 |
| Fei Liu | 2 | 345 | 23.90 |