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We evaluate named entity representations of BERT-based NLP models byinvestigating their robustness to replacements from the same typed class in theinput. We highlight that on several tasks while such perturbations are natural,state of the art trained models are surprisingly brittle. The brittlenesscontinues even with the recent entity-aware BERT models. We also try to discernthe cause of this non-robustness, considering factors such as tokenization andfrequency of occurrence. Then we provide a simple method that ensemblespredictions from multiple replacements while jointly modeling the uncertaintyof type annotations and label predictions. Experiments on three NLP tasks showthat our method enhances robustness and increases accuracy on both natural andadversarial datasets.

What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?