LATIDIA · Ciberseguridad

Calpric: Etiquetado inclusivo y de grano fino de las políticas de privacidad con crowdsourcing y aprendizaje activo

arXiv:2008.02954v2 Tipo de anuncio: reemplazar Resumen: Un desafío importante para capacitar modelos precisos de aprendizaje profundo sobre políticas de privacidad es el costo y la dificultad de obtener un conjunto amplio e integral de capacitación

La noticia

arXiv:2008.02954v2 Announce Type: replace Abstract: A significant challenge to training accurate deep learning models on privacy policies is the cost and difficulty of obtaining a large and comprehensive set of training data. To address these challenges, we present Calpric, which combines automatic text selection and segmentation, active learning and the use of crowdsourced annotators to generate a large, balanced training set for privacy policies at low cost. Automated text selection and segmentation simplify the labeling task, enabling untrained annotators from crowdsourcing platforms, like Amazon's Mechanical Turk, to be competitive with trained annotators, such as law students, and also reduce inter-annotator disagreement, which decreases labeling cost. Having reliable labels for training