Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stronger Baselines for Grammatical Error Correction Using Pretrained
  Encoder-Decoder Model   Studies on grammatical error correction (GEC) have reported the effectiveness
of pretraining a Seq2Seq model with a large amount of pseudodata. However, this
approach requires time-consuming pretraining for GEC because of the size of the
pseudodata. In this study, we explore the utility of bidirectional and
auto-regressive transformers (BART) as a generic pretrained encoder-decoder
model for GEC. With the use of this generic pretrained model for GEC, the
time-consuming pretraining can be eliminated. We find that monolingual and
multilingual BART models achieve high performance in GEC, with one of the
results being comparable to the current strong results in English GEC. Our
implementations are publicly available at GitHub
(https://github.com/Katsumata420/generic-pretrained-GEC).

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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: math