A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Proceedings of the Second Workshop on Privacy in NLP
Online services utilize privacy settings to provide users with control over their data. However, these privacy settings are often hard to locate, causing the user to rely on provider-chosen default values. In this work, we train privacy-settings-centric encoders and leverage them to create an interface that allows users to search for privacy settings using free-form queries. In order to achieve this goal, we create a custom Semantic Similarity dataset, which consists of real user queriesdoi:10.18653/v1/2020.privatenlp-1.4 fatcat:byfbotsugffxhhqlqvkhv4slrq