  Robustness to capitalization errors is a highly desirable characteristic of
named entity recognizers, yet we find standard models for the task are
surprisingly brittle to such noise. Existing methods to improve robustness to
the noise completely discard given orthographic information, mwhich
significantly degrades their performance on well-formed text. We propose a
simple alternative approach based on data augmentation, which allows the model
to \emph{learn} to utilize or ignore orthographic information depending on its
usefulness in the context. It achieves competitive robustness to capitalization
errors while making negligible compromise to its performance on well-formed
text and significantly improving generalization power on noisy user-generated
text. Our experiments clearly and consistently validate our claim across
different types of machine learning models, languages, and dataset sizes.
