Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples
  for Relation Extraction   Recent neural-based relation extraction approaches, though achieving
promising improvement on benchmark datasets, have reported their vulnerability
towards adversarial attacks. Thus far, efforts mostly focused on generating
adversarial samples or defending adversarial attacks, but little is known about
the difference between normal and adversarial samples. In this work, we take
the first step to leverage the salience-based method to analyze those
adversarial samples. We observe that salience tokens have a direct correlation
with adversarial perturbations. We further find the adversarial perturbations
are either those tokens not existing in the training set or superficial cues
associated with relation labels. To some extent, our approach unveils the
characters against adversarial samples. We release an open-source testbed,
"DiagnoseAdv" in https://github.com/zjunlp/DiagnoseAdv.

3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas