Adversarial Evaluation of BERT for Biomedical Named Entity Recognition
The success of pre-trained word embeddings of the BERT model has motivated its use in tasks in the biomedical domain. However, it is not clear if this model works correctly in real scenarios. In this work, we propose an adversarial evaluation scheme in a BioNER dataset, which consists of two types of attacks inspired by natural spelling errors and synonyms of medical terms. Our results indicate that under these adversarial settings, the performance of the models drops significantly. Despite the result, we show how the robustness of the models can be significantly improved by training them with adversarial examples.
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