A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Table 1: Overview of the datasets used.
['[EMPTY]', 'Training', 'Validation', 'Development', 'Test']
We evaluate the performance of the models on the standard sets from the CoNLL-14 shared task Ng et al. We report final performance on the CoNLL-14 test set without alternatives, and analyze model performance on the CoNLL-13 development set Dahlmeier et al. We use the development and validation sets for model selection. We report performance in F0.5-measure, as calculated by the m2scorer—the official implementation of the scoring metric in the shared task. Given system outputs and gold-standard edits, m2scorer computes the F0.5 measure of a set of system edits against a set of gold-standard edits.