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Protecting NLP models against misspellings whether accidental or adversarialhas been the object of research interest for the past few years. Existingremediations have typically either compromised accuracy or required full modelre-training with each new class of attacks. We propose a novel method ofretroactively adding resilience to misspellings to transformer-based NLPmodels. This robustness can be achieved without the need for re-training of theoriginal NLP model and with only a minimal loss of language understandingperformance on inputs without misspellings. Additionally we propose a newefficient approximate method of generating adversarial misspellings, whichsignificantly reduces the cost needed to evaluate a model's resilience toadversarial attacks.

MockingBERT: A Method for Retroactively Adding Resilience to NLP Models