RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation
This addresses the reliability of GEC systems for users in daily writing tasks, but it is incremental as it builds on existing benchmarks and methods.
The authors tackled the problem of Grammatical Error Correction (GEC) systems failing when inputs are slightly modified, by introducing RobustGEC, a benchmark with 5,000 cases, and found that state-of-the-art systems lack robustness, proposing a method to address this.
Grammatical Error Correction (GEC) systems play a vital role in assisting people with their daily writing tasks. However, users may sometimes come across a GEC system that initially performs well but fails to correct errors when the inputs are slightly modified. To ensure an ideal user experience, a reliable GEC system should have the ability to provide consistent and accurate suggestions when encountering irrelevant context perturbations, which we refer to as context robustness. In this paper, we introduce RobustGEC, a benchmark designed to evaluate the context robustness of GEC systems. RobustGEC comprises 5,000 GEC cases, each with one original error-correct sentence pair and five variants carefully devised by human annotators. Utilizing RobustGEC, we reveal that state-of-the-art GEC systems still lack sufficient robustness against context perturbations. In addition, we propose a simple yet effective method for remitting this issue.