Neural Language Correction with Character-Based Attention
Table 6: CoNLL development set recall for 5 most frequent error categories with and without training on data with synthesized article/determiner and noun number errors. Wci denotes wrong collocation/idiom errors.
['Type', 'Count', '[ITALIC] R no aug', '[ITALIC] R aug']
Effects of Data Augmentation We obtain promising improvements using data augmentation, boosting F0.5-score on the development set from 31.55 to 34.81. The same phenomenon has been observed by \newciterozovskaya2012ui. Interestingly, the recall of other error types (see \newciteng2014conll for descriptions) decreases. We surmise this is because the additional training data contains only ArtOrDet and Nn errors, and hence the network is encouraged to simply copy the output when those error types are not present. We hope synthesizing data with a variety of other error types may fix this issue and improve performance.