Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Near Human-Level Performance in Grammatical Error Correction with Hybrid
  Machine Translation   We combine two of the most popular approaches to automated Grammatical Error
Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC
based on Neural Machine Translation (NMT). The hybrid system achieves new
state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC
system preserves the accuracy of SMT output and, at the same time, generates
more fluent sentences as it typical for NMT. Our analysis shows that the
created systems are closer to reaching human-level performance than any other
GEC system reported so far.

17
The categories of this paper are: q-fin
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an