Papers › GECToR -- Grammatical Error Correction: Tag, Not Rewrite
GECToR -- Grammatical Error Correction: Tag, Not Rewrite
Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub, Oleksandr Skurzhanskyi
In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an F_(0.5) of 65.3/66.5 on CoNLL-2014 (test) and F_(0.5) of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system. The code and trained models are publicly available.
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Code
Syntology Ran 2 of 12 code samples harvested from 2 repositories linked to this paper; 10 have no recorded run. Of those that ran: 1 ran · violated contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
12 samples harvested; 2 ran; 0 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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) | Sequence tagging + token-level transformations + two-stage fine-tuning (+RoBERTa, XLNet) | F0.5 | 73.7 | #9 of 19 | Archive leaderboard | report |
| Grammatical Error Correction | BEA-2019 (test) | Sequence tagging + token-level transformations + two-stage fine-tuning (+XLNet) | F0.5 | 72.4 | #13 of 19 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | Sequence tagging + token-level transformations + two-stage fine-tuning (+BERT, RoBERTa, XLNet) | F0.5 | 66.5 | #10 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | Sequence tagging + token-level transformations + two-stage fine-tuning (+BERT, RoBERTa, XLNet) | Precision | 78.2 | #10 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | Sequence tagging + token-level transformations + two-stage fine-tuning (+BERT, RoBERTa, XLNet) | Recall | 41.5 | #10 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | Sequence tagging + token-level transformations + two-stage fine-tuning (+XLNet) | F0.5 | 65.3 | #12 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | Sequence tagging + token-level transformations + two-stage fine-tuning (+XLNet) | Precision | 77.5 | #12 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | Sequence tagging + token-level transformations + two-stage fine-tuning (+XLNet) | Recall | 40.1 | #12 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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