  Privacy protection raises great attention on both legal levels and user
awareness. To protect user privacy, countries enact laws and regulations
requiring software privacy policies to regulate their behavior. However,
privacy policies are written in natural languages with many legal terms and
software jargon that prevent users from understanding and even reading them. It
is desirable to use NLP techniques to analyze privacy policies for helping
users understand them. Furthermore, existing datasets ignore law requirements
and are limited to English. In this paper, we construct the first Chinese
privacy policy dataset, namely CA4P-483, to facilitate the sequence labeling
tasks and regulation compliance identification between privacy policies and
software. Our dataset includes 483 Chinese Android application privacy
policies, over 11K sentences, and 52K fine-grained annotations. We evaluate
families of robust and representative baseline models on our dataset. Based on
baseline performance, we provide findings and potential research directions on
our dataset. Finally, we investigate the potential applications of CA4P-483
combing regulation requirements and program analysis.
