Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Minimal Supervision BERT-based Grammar Error Correction   Current grammatical error correction (GEC) models typically consider the task
as sequence generation, which requires large amounts of annotated data and
limit the applications in data-limited settings. We try to incorporate
contextual information from pre-trained language model to leverage annotation
and benefit multilingual scenarios. Results show strong potential of
Bidirectional Encoder Representations from Transformers (BERT) in grammatical
error correction task.

11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin