Auxiliary Objectives for Neural Error Detection Models
Table 3: Error detection results on the CoNLL-14 test set using different auxiliary loss functions.
['[EMPTY]', 'predicted', 'CoNLL-14 TEST1 correct', 'CoNLL-14 TEST1 P', 'CoNLL-14 TEST1 R', 'CoNLL-14 TEST1  [ITALIC] F0.5', 'CoNLL-14 TEST2 correct', 'CoNLL-14 TEST2 P', 'CoNLL-14 TEST2 R', 'CoNLL-14 TEST2  [ITALIC] F0.5']
Word frequency and L1 have nearly no effect, whereas the fine-grained error labels lead to roughly 2% absolute improvement over the basic system. The inclusion of POS tags in the auxiliary objective consistently leads to the highest F0.5. While GRs also improve performance over the main system, their overall contribution is less compared to the FCE test set, which can be explained by the different writing style in the CoNLL data.