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There is an increasing amount of literature that claims the brittleness ofdeep neural networks in dealing with adversarial examples that are createdmaliciously. It is unclear, however, how the models will perform in realisticscenarios where \textit{natural rather than malicious} adversarial instancesoften exist. This work systematically explores the robustness of BERT, thestate-of-the-art Transformer-style model in NLP, in dealing with noisy data,particularly mistakes in typing the keyboard, that occur inadvertently.Intensive experiments on sentiment analysis and question answering benchmarksindicate that: (i) Typos in various words of a sentence do not influenceequally. The typos in informative words make severer damages; (ii) Mistype isthe most damaging factor, compared with inserting, deleting, etc.; (iii) Humansand machines have different focuses on recognizing adversarial attacks.

Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT