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Assessing Unintended Memorization in Neural Discriminative Sequence Models

Abstract : Despite their success in a multitude of tasks, neural models trained on natural language have been shown to memorize the intricacies of their training data, posing a potential privacy threat. In this work, we propose a metric to quantify unintended memorization in neural dis-criminative sequence models. The proposed metric, named d-exposure (discriminative exposure), utilizes language ambiguity and classification confidence to elicit the model's propensity to memorization. Through experimental work on a named entity recognition task, we show the validity of d-exposure to measure memorization. In addition, we show that d-exposure is not a measure of overfitting as it does not increase when the model overfits.
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Submitted on : Thursday, June 25, 2020 - 5:20:08 PM
Last modification on : Saturday, June 27, 2020 - 3:09:27 AM
Long-term archiving on: : Wednesday, September 23, 2020 - 3:44:26 PM


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  • HAL Id : hal-02880581, version 1



Mossad Helali, Thomas Kleinbauer, Dietrich Klakow. Assessing Unintended Memorization in Neural Discriminative Sequence Models. 23rd International Conference on Text, Speech and Dialogue, Sep 2020, Brno, Czech Republic. ⟨hal-02880581⟩



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