Papers › Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task
Marcin Junczys-Dowmunt, Roman Grundkiewicz, Shubha Guha, Kenneth Heafield
Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M² on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.
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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 | Transformer | F0.5 | 55.8 | #22 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | JFLEG | Transformer | GLEU | 59.9 | #5 of 6 | Archive leaderboard | report |
| Grammatical Error Correction | Restricted | Transformer | F0.5 | 55.8 | #3 of 4 | Archive leaderboard | report |
| Grammatical Error Correction | _Restricted_ | Transformer | GLEU | 59.9 | #1 of 2 | 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.
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