Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Breaking BERT: Understanding its Vulnerabilities for Named Entity
  Recognition through Adversarial Attack   Both generic and domain-specific BERT models are widely used for natural
language processing (NLP) tasks. In this paper we investigate the vulnerability
of BERT models to variation in input data for Named Entity Recognition (NER)
through adversarial attack. Experimental results show that BERT models are
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. BERT models seem most vulnerable to changes in the local
context of entities and often a single change is sufficient to fool the model.
The domain-specific BERT model trained from scratch (SciBERT) is more
vulnerable than the original BERT model or the domain-specific model that
retains the BERT vocabulary (BioBERT). We also find that BERT models are
particularly vulnerable to emergent entities. Our results chart the
vulnerabilities of BERT models for NER and emphasize the importance of further
research into uncovering and reducing these weaknesses.

2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th