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            <namePart type="given">Kam-Fai</namePart>
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    <abstract>Studies on grammatical error correction (GEC) have reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining of GEC because of the size of the pseudodata. In this study, we explored 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.</abstract>
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%0 Conference Proceedings
%T Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model
%A Katsumata, Satoru
%A Komachi, Mamoru
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F katsumata-komachi-2020-stronger
%X Studies on grammatical error correction (GEC) have reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining of GEC because of the size of the pseudodata. In this study, we explored 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.
%R 10.18653/v1/2020.aacl-main.83
%U https://aclanthology.org/2020.aacl-main.83
%U https://doi.org/10.18653/v1/2020.aacl-main.83
%P 827-832

Markdown (Informal)

[Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model](https://aclanthology.org/2020.aacl-main.83) (Katsumata & Komachi, AACL 2020)

• Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model (Katsumata & Komachi, AACL 2020)

ACL

• Satoru Katsumata and Mamoru Komachi. 2020. Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 827–832, Suzhou, China. Association for Computational Linguistics.