Breaking BERT: Understanding its Vulnerabilities for Biomedical Named Entity Recognition through Adversarial Attack
Biomedical named entity recognition (NER) is a key task in the extraction of information from biomedical literature and electronic health records. For this task, both generic and biomedical BERT models are widely used. Robustness of these models is vital for medical applications, such as automated medical decision making. In this paper we investigate the vulnerability of BERT models to variation in input data for NER through adversarial attack. Since adversarial attack methods for NER are sparse, we propose two black-box methods for NER based on existing methods for classification tasks. Experimental results show that the original as well as the biomedical BERT models are highly vulnerable to entity replacement: They can be fooled in 89.2 to 99.4% of the cases to mislabel previously correct entities. BERT models are also vulnerable to variation in the entity context with 20.2 to 45.0% of entities predicted completely wrong and another 29.3 to 53.3% of entities predicted wrong partially. Often a single change is sufficient to fool the model. BERT models seem most vulnerable to changes in the local context of entities. Of the biomedical BERT models, the vulnerability of BioBERT is comparable to the original BERT model whereas SciBERT is even more vulnerable. Our results chart the vulnerabilities of BERT models for biomedical NER and emphasize the importance of further research into uncovering and reducing these weaknesses.