Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Personalizing Grammatical Error Correction: Adaptation to Proficiency
  Level and L1   Grammar error correction (GEC) systems have become ubiquitous in a variety of
software applications, and have started to approach human-level performance for
some datasets. However, very little is known about how to efficiently
personalize these systems to the user's characteristics, such as their
proficiency level and first language, or to emerging domains of text. We
present the first results on adapting a general-purpose neural GEC system to
both the proficiency level and the first language of a writer, using only a few
thousand annotated sentences. Our study is the broadest of its kind, covering
five proficiency levels and twelve different languages, and comparing three
different adaptation scenarios: adapting to the proficiency level only, to the
first language only, or to both aspects simultaneously. We show that tailoring
to both scenarios achieves the largest performance improvement (3.6 F0.5)
relative to a strong baseline.

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The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con