  Sensitivity of deep-neural models to input noise is known to be a challenging
problem. In NLP, model performance often deteriorates with naturally occurring
noise, such as spelling errors. To mitigate this issue, models may leverage
artificially noised data. However, the amount and type of generated noise has
so far been determined arbitrarily. We therefore propose to model the errors
statistically from grammatical-error-correction corpora. We present a thorough
evaluation of several state-of-the-art NLP systems' robustness in multiple
languages, with tasks including morpho-syntactic analysis, named entity
recognition, neural machine translation, a subset of the GLUE benchmark and
reading comprehension. We also compare two approaches to address the
performance drop: a) training the NLP models with noised data generated by our
framework; and b) reducing the input noise with external system for natural
language correction. The code is released at https://github.com/ufal/kazitext.
