Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction
Table 4: Feature ablation test.
['[BOLD] Features', '[BOLD] Accuracy']
We first evaluate our edit classifier separately, independent of the entire GEC system. In order to do this, we compute the accuracy of the classifier on the edits obtained from the CoNLL-2013 dataset. Since we use the CoNLL-2013 dataset for threshold tuning, we evaluate the classifier before threshold tuning. We perform ablation testing, by removing each category of features: SMT features, lexical and POS features, context features, and language model features.