Papers › Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
Satoru Katsumata, Mamoru Komachi
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Grammatical Error Correction | CoNLL-2014 Shared Task | BART | F0.5 | 63.0 | #16 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | BART | Precision | 69.9 | #16 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | BART | Recall | 45.1 | #16 of 23 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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