Papers › Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
Efficient and Interpretable Grammatical Error Correction with Mixture of Experts
Muhammad Reza Qorib, Alham Fikri Aji, Hwee Tou Ng
Error type information has been widely used to improve the performance of grammatical error correction (GEC) models, whether for generating corrections, re-ranking them, or combining GEC models. Combining GEC models that have complementary strengths in correcting different error types is very effective in producing better corrections. However, system combination incurs a high computational cost due to the need to run inference on the base systems before running the combination method itself. Therefore, it would be more efficient to have a single model with multiple sub-networks that specialize in correcting different error types. In this paper, we propose a mixture-of-experts model, MoECE, for grammatical error correction. Our model successfully achieves the performance of T5-XL with three times fewer effective parameters. Additionally, our model produces interpretable corrections by also identifying the error type during inference.
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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) | MoECE | F0.5 | 74.07 | #8 of 19 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | MoECE | F0.5 | 67.79 | #8 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | MoECE | Precision | 74.29 | #8 of 23 | Archive leaderboard | report |
| Grammatical Error Correction | CoNLL-2014 Shared Task | MoECE | Recall | 50.21 | #8 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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