Disentangling the Linguistic Competence of Privacy-Preserving BERT

• URL: http://arxiv.org/abs/2310.11363v1
• Date: Tue, 17 Oct 2023 16:00:26 GMT
• Title: Disentangling the Linguistic Competence of Privacy-Preserving BERT
• Authors: Stefan Arnold, Nils Kemmerzell, and Annika Schreiner
• Abstract summary: Differential Privacy (DP) has been tailored to address the unique challenges of text-to-text privatization. We employ a series of interpretation techniques on the internal representations extracted from BERT trained on perturbed pre-text. Using probing tasks to unpack this dissimilarity, we find evidence that text-to-text privatization affects the linguistic competence across several formalisms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differential Privacy (DP) has been tailored to address the unique challenges of text-to-text privatization. However, text-to-text privatization is known for degrading the performance of language models when trained on perturbed text. Employing a series of interpretation techniques on the internal representations extracted from BERT trained on perturbed pre-text, we intend to disentangle at the linguistic level the distortion induced by differential privacy. Experimental results from a representational similarity analysis indicate that the overall similarity of internal representations is substantially reduced. Using probing tasks to unpack this dissimilarity, we find evidence that text-to-text privatization affects the linguistic competence across several formalisms, encoding localized properties of words while falling short at encoding the contextual relationships between spans of words.

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