The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction
Table 2: (Exp3) Using only NUCLE as base training for correction. The AEG models are trained using NUCLE-CLC data as in other experiments.
['[BOLD] Model', '[BOLD] NUCLE', '[BOLD] +10K', '[BOLD] +50K', '[BOLD] +1M']
Exp3: We can see that the performance of all models, except ON-LSTM, improves significantly when 1 million artificial sentence pairs are added to the NUCLE training data, even though the errors in these sentences do not resemble natural errors. where the performance of the GEC models trained with the combination of artificial error and CLC-NUCLE base data drops. This suggests that artificial data is helpful when the base data size is relatively small.