COCOGEC: Counterfactual Generation for Robust Grammatical Error Correction (2026.findings-acl)
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| Challenge: | Existing GEC models fail to understand error patterns in varying contexts . a framework that generates copies of training instances with error-irrelevant contexts altered is proposed . |
| Approach: | They propose a framework that generates copies of training instances with error-irrelevant contexts altered. |
| Outcome: | The proposed framework outperforms baselines on the simulated tasks and outperformed existing models. |
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| Challenge: | Grammatical Error Correction (GEC) systems perform well in academic benchmarks, but in practical applications they may not correct errors when users perform irrelevant modifications. |
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| Challenge: | Empirical evaluation shows that MainGEC achieves consistent and significant performance improvements on two benchmark datasets. |
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