Disentangling the Linguistic Competence of Privacy-Preserving BERT
Stefan Arnold Nils Kemmerzell Annika Schreiner
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.