A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification
Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu
Abstract
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 professional languages with many legal terms and software jargon that prevent users from understanding and even reading them. It is necessary and urgent to use NLP techniques to analyze privacy policies. However, 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.
BibTeX
@inproceedings{zhao-etal-2022-fine-grained,
title = "A Fine-grained {C}hinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification",
author = "Zhao, Kaifa and
Yu, Le and
Zhou, Shiyao and
Li, Jing and
Luo, Xiapu and
Chiu, Yat Fei Aemon and
Liu, Yutong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.700/",
doi = "10.18653/v1/2022.emnlp-main.700",
pages = "10266--10277"
}