Papers › Frustratingly Easy System Combination for Grammatical Error Correction
Frustratingly Easy System Combination for Grammatical Error Correction
Muhammad Qorib, Seung-Hoon Na, Hwee Tou Ng
In this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification. We demonstrate that with the right problem formulation, a simple logistic regression algorithm can be highly effective for combining GEC models. Our method successfully increases the F0.5 score from the highest base GEC system by 4.2 points on the CoNLL-2014 test set and 7.2 points on the BEA-2019 test set. Furthermore, our method outperforms the state of the art by 4.0 points on the BEA-2019 test set, 1.2 points on the CoNLL-2014 test set with original annotation, and 3.4 points on the CoNLL-2014 test set with alternative annotation. We also show that our system combination generates better corrections with higher F0.5 scores than the conventional ensemble.
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 | BEA-2019 (test) | ESC | F0.5 | 79.90 | #3 of 19 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | ESC | F0.5 | 69.51 | #6 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | ESC | Precision | 81.48 | #6 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | ESC | Recall | 43.78 | #6 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.
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