Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sublanguage: A Serious Issue Affects Pretrained Models in Legal Domain   Legal English is a sublanguage that is important for everyone but not for
everyone to understand. Pretrained models have become best practices among
current deep learning approaches for different problems. It would be a waste or
even a danger if these models were applied in practice without knowledge of the
sublanguage of the law. In this paper, we raise the issue and propose a trivial
solution by introducing BERTLaw a legal sublanguage pretrained model. The
paper's experiments demonstrate the superior effectiveness of the method
compared to the baseline pretrained model

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The categories of this paper are: solv-int
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The categories of this paper are: physics
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
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The categories of this paper are: econ
2
The categories of this paper are: math