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Question Answering for Privacy Policies: Combining Computational and Legal Perspectives

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arxiv 1911.00841 v1 pith:NNOEBRCV submitted 2019-11-03 cs.CL

Abhilasha Ravichander , Alan W Black , Shomir Wilson , Thomas Norton , Norman Sadeh This is my paper · ORCID
classification cs.CL
keywords questionansweringcorpuspoliciesprivacyprivacyqaissueslegal
verification ladder T0 review T1 audit T2 compute T3 formal
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Privacy policies are long and complex documents that are difficult for users to read and understand, and yet, they have legal effects on how user data is collected, managed and used. Ideally, we would like to empower users to inform themselves about issues that matter to them, and enable them to selectively explore those issues. We present PrivacyQA, a corpus consisting of 1750 questions about the privacy policies of mobile applications, and over 3500 expert annotations of relevant answers. We observe that a strong neural baseline underperforms human performance by almost 0.3 F1 on PrivacyQA, suggesting considerable room for improvement for future systems. Further, we use this dataset to shed light on challenges to question answerability, with domain-general implications for any question answering system. The PrivacyQA corpus offers a challenging corpus for question answering, with genuine real-world utility.

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Cited by 5 Pith papers

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  4. AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System

    cs.CL 2026-07 conditional novelty 4.0 of 10

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