We demonstrate that error generation is much less sensitive to parsing errors and irregularities than rule-based systems for detecting subject-verb agreement.
artificial error generation enables us to utilise much more training data, and therefore can develop more robust neural models for SVA error detection that do not overfit the available, manually annotated training data.