CoCoGEC: Counterfactual Generation for Robust Grammatical Error Correction

arXiv:2606.15069v1 Announce Type: new Abstract: Grammatical error correction (GEC) systems are usually trained and evaluated on GEC benchmarks, but their performance often drops sharply once the surrounding context is slightly perturbed or extended. This indicates that the existing GEC models usually fail to understand the error patterns in the varying contexts. In this paper, we thoroughly investigate the counterfactuals for GEC tasks, where the subtle changes to the contexts could lead to the label flipping issue. We propose CoCoGEC, a counterfactual generation framework that creates copies

Source: arXiv cs.CL — read the full report at the original publisher.

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