Compositional Sequence Labeling Models for Error Detection in Learner Writing
Table 2: Results on the public FCE test set when incrementally providing more training data to the error detection model.
['Training data', 'Dev  [ITALIC] F0.5', 'Test  [ITALIC] F0.5']
We found that incorporating the NUCLE dataset does not improve performance over using only the FCE-public dataset, which is likely due to the two corpora containing texts with different domains and writing styles. The texts in FCE are written by young intermediate students, in response to prompts eliciting letters, emails and reviews, whereas NUCLE contains mostly argumentative essays written by advanced adult learners. The differences in the datasets offset the benefits from additional training data, and the performance remains roughly the same.