Learning to combine Grammatical Error Corrections
Table 5: Size of synthetic datasets and Nematus scores when trained on them.
['Data Source', 'Size (sentences)', '[ITALIC] F0.5']
We also tried training Nematus over synthetic errors data. We generated errors using data from two different domains. Books from project Gutenberg and gold sentences from W&I train set. These experiments show that relying on the source domain is crucial and it is best to generate data using text from similar domain. When using the synthetic W&I train set we reached a score that is just a little lower than the score when training over W&I train set directly (0.19 vs 0.23). This might suggest that there is potential in using synthetic data when combined with other data sets and promise for synthetic data methods for unsupervised GEC.