  Named Entity Recognition (NER) systems often demonstrate great performance on
in-distribution data, but perform poorly on examples drawn from a shifted
distribution. One way to evaluate the generalization ability of NER models is
to use adversarial examples, on which the specific variations associated with
named entities are rarely considered. To this end, we propose leveraging
expert-guided heuristics to change the entity tokens and their surrounding
contexts thereby altering their entity types as adversarial attacks. Using
expert-guided heuristics, we augmented the CoNLL 2003 test set and manually
annotated it to construct a high-quality challenging set. We found that
state-of-the-art NER systems trained on CoNLL 2003 training data drop
performance dramatically on our challenging set. By training on adversarial
augmented training examples and using mixup for regularization, we were able to
significantly improve the performance on the challenging set as well as improve
out-of-domain generalization which we evaluated by using OntoNotes data. We
have publicly released our dataset and code at
https://github.com/GT-SALT/Guided-Adversarial-Augmentation.
