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

A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification