  Protecting NLP models against misspellings whether accidental or adversarial
has been the object of research interest for the past few years. Existing
remediations have typically either compromised accuracy or required full model
re-training with each new class of attacks. We propose a novel method of
retroactively adding resilience to misspellings to transformer-based NLP
models. This robustness can be achieved without the need for re-training of the
original NLP model and with only a minimal loss of language understanding
performance on inputs without misspellings. Additionally we propose a new
efficient approximate method of generating adversarial misspellings, which
significantly reduces the cost needed to evaluate a model's resilience to
adversarial attacks.

Computation and Language