Evaluation of large-scale synthetic data for Grammar Error Correction
This work addresses the need for better evaluation methods in GEC data generation, offering incremental improvements for researchers and practitioners in natural language processing.
The paper tackled the problem of evaluating synthetic data quality for Grammar Error Correction by introducing three metrics—reliability, diversity, and distribution match—to provide insights beyond system performance, enabling automatic evaluation and feedback for data generation systems.
Grammar Error Correction(GEC) mainly relies on the availability of high quality of large amount of synthetic parallel data of grammatically correct and erroneous sentence pairs. The quality of the synthetic data is evaluated on how well the GEC system performs when pre-trained using it. But this does not provide much insight into what are the necessary factors which define the quality of these data. So this work aims to introduce 3 metrics - reliability, diversity and distribution match to provide more insight into the quality of large-scale synthetic data generated for the GEC task, as well as automatically evaluate them. Evaluating these three metrics automatically can also help in providing feedback to the data generation systems and thereby improve the quality of the synthetic data generated dynamically