Furthermore, an RL agent can perturb parts of an embedded text for preserving both utility and privacy, instead of retraining an embedding as in adversarial learning.
we propose a novel Reinforcement Learning-based Text Anonymizer, namely, RLTA, composed of two main components: 1) an attention based task-aware text representation learner to extract latent embedding representation of the original text's content w.r.t.