A Simple but Effective Classification Model for Grammatical Error Correction
Table 2: Results of our neural classification model, with and without attention, on CoNLL-2014 data (based on the combination of two annotators without alternative answers).
['[BOLD] Error Type', '[BOLD] Baseline', '[BOLD] Attention']
For all the error types, we can see that using attention achieves better performance, suggesting the effectiveness of modeling the interaction among words in the context, or between the target word and other words.