Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural language models for network configuration: Opportunities and
  reality check   Boosted by deep learning, natural language processing (NLP) techniques have
recently seen spectacular progress, mainly fueled by breakthroughs both in
representation learning with word embeddings (e.g. word2vec) as well as novel
architectures (e.g. transformers). This success quickly invited researchers to
explore the use of NLP techniques to other field, such as computer programming
languages, with the promise to automate tasks in software programming (bug
detection, code synthesis, code repair, cross language translation etc.). By
extension, NLP has potential for application to network configuration languages
as well, for instance considering tasks such as network configuration
verification, synthesis, and cross-vendor translation. In this paper, we survey
recent advances in deep learning applied to programming languages, for the
purpose of code verification, synthesis and translation: in particularly, we
review their training requirements and expected performance, and qualitatively
assess whether similar techniques can benefit corresponding use-cases in
networking.

18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci