Papers › Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation
Roman Grundkiewicz, Marcin Junczys-Dowmunt
We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.
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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 | SMT + BiGRU | F0.5 | 56.25 | #21 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task (10 annotations) | SMT + BiGRU | F0.5 | 72.04 | #2 of 3 | Archive leaderboard | report |
| Grammatical Error Correction | JFLEG | SMT + BiGRU | GLEU | 61.5 | #3 of 6 | 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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