Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Unsupervised Grammatical Error Correction using Statistical
  Machine Translation with Synthetic Comparable Corpus   We introduce unsupervised techniques based on phrase-based statistical
machine translation for grammatical error correction (GEC) trained on a pseudo
learner corpus created by Google Translation. We verified our GEC system
through experiments on various GEC dataset, includi ng a low resource track of
the shared task at Building Educational Applications 2019 (BEA 2019). As a
result, we achieved an F_0.5 score of 28.31 points with the test data of the
low resource track.

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The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat