  Neural networks and in particular the attention mechanism have brought
significant advances to the field of Automated Essay Scoring. Many of these
systems use a regression-based model which may be prone to underfitting when
the model only predicts the mean of the training data. In this paper, we
present a dynamic loss function that creates an incentive for the model to
predict with the correct distribution, as well as predicting the correct
values. Our loss function achieves this goal without sacrificing any
performance achieving a Quadratic Weighted Kappa score of 0.752 on the
Automated Student Assessment Prize Automated Essay Scoring dataset.
