Auxiliary Objectives for Neural Error Detection Models
Table 6: Error detection results using auxiliary objectives, trained on additional data.
['[EMPTY]', 'R&Y  [ITALIC] F0.5', 'P', 'R', '[ITALIC] F0.5']
(R&Y). Since most of the large training set comes from the CLC, which is quite similar to the FCE dataset, it is likely that the available training data is sufficient and the auxiliary objective does not offer an additional benefit. However, there are considerable improvements on the CoNLL test sets, with 1.8% and 1.1% absolute improvements on the corresponding benchmarks. Only small amounts of the training data are similar to the CoNLL dataset, and including the auxiliary objective has provided a more robust model that delivers better performance on different writing styles.