Encode, Tag, Realize: High-Precision Text Editing
Table 5: Results on grammatical-error correction. Note that grundkiewicz2019neural augment the training dataset of 4,384 examples by 100 million synthetic examples and 2 million Wikipedia edits.
['Model', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F0.5']
Again, the tagging approach clearly outperforms the BERT-based seq2seq model, here by being more than seven times as accurate in the prediction of corrections. This can be accounted to the seq2seq model’s much richer generation capacity, which the model can not properly tune to the task at hand given the small amount of training data. The tagging approach on the other hand is naturally suited to this kind of problem.