  This paper investigates the robustness of NLP against perturbed word forms.
While neural approaches can achieve (almost) human-like accuracy for certain
tasks and conditions, they often are sensitive to small changes in the input
such as non-canonical input (e.g., typos). Yet both stability and robustness
are desired properties in applications involving user-generated content, and
the more as humans easily cope with such noisy or adversary conditions. In this
paper, we study the impact of noisy input. We consider different noise
distributions (one type of noise, combination of noise types) and mismatched
noise distributions for training and testing. Moreover, we empirically evaluate
the robustness of different models (convolutional neural networks, recurrent
neural networks, non-neural models), different basic units (characters, byte
pair encoding units), and different NLP tasks (morphological tagging, machine
translation).
