Papers › Improved grammatical error correction by ranking elementary edits
Improved grammatical error correction by ranking elementary edits
Anonymous
We offer a rescoring method for grammatical error correction which is based on two-stage procedure: the first stage model extracts local edits and the second classiifies them as correct or false. We show how to use an encoder-decoder or sequence labeling approach as the first stage of our model. We achieve state-of-the-art quality on BEA 2019 English dataset even with a weak BERT-GEC basic model. When using a state-of-the-art GECToR edit generator and the combined scorer, our model beats GECToR on BEA 2019 by 2-3%. Our model also beats previous state-of-the-art on Russian, despite using smaller models and less data than the previous approaches.
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) | clang_large_ft2-gector | F0.5 | 77.1 | #5 of 19 | 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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