Judge a Sentence by Its Content to Generate Grammatical Errors
This addresses data scarcity for GEC researchers and practitioners, but it is incremental as it builds on existing synthetic data generation approaches.
The paper tackles the problem of data sparsity in grammatical error correction (GEC) by proposing a two-stage method to generate synthetic training data that allows sentences with multiple errors, based on sentence merit, and shows that a model trained on this data outperforms prior synthetic data methods.
Data sparsity is a well-known problem for grammatical error correction (GEC). Generating synthetic training data is one widely proposed solution to this problem, and has allowed models to achieve state-of-the-art (SOTA) performance in recent years. However, these methods often generate unrealistic errors, or aim to generate sentences with only one error. We propose a learning based two stage method for synthetic data generation for GEC that relaxes this constraint on sentences containing only one error. Errors are generated in accordance with sentence merit. We show that a GEC model trained on our synthetically generated corpus outperforms models trained on synthetic data from prior work.