  We present FireBERT, a set of three proof-of-concept NLP classifiers hardened
against TextFooler-style word-perturbation by producing diverse alternatives to
original samples. In one approach, we co-tune BERT against the training data
and synthetic adversarial samples. In a second approach, we generate the
synthetic samples at evaluation time through substitution of words and
perturbation of embedding vectors. The diversified evaluation results are then
combined by voting. A third approach replaces evaluation-time word substitution
with perturbation of embedding vectors. We evaluate FireBERT for MNLI and IMDB
Movie Review datasets, in the original and on adversarial examples generated by
TextFooler. We also test whether TextFooler is less successful in creating new
adversarial samples when manipulating FireBERT, compared to working on
unhardened classifiers. We show that it is possible to improve the accuracy of
BERT-based models in the face of adversarial attacks without significantly
reducing the accuracy for regular benchmark samples. We present co-tuning with
a synthetic data generator as a highly effective method to protect against 95%
of pre-manufactured adversarial samples while maintaining 98% of original
benchmark performance. We also demonstrate evaluation-time perturbation as a
promising direction for further research, restoring accuracy up to 75% of
benchmark performance for pre-made adversarials, and up to 65% (from a baseline
of 75% orig. / 12% attack) under active attack by TextFooler.
