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Neural networks and in particular the attention mechanism have broughtsignificant advances to the field of Automated Essay Scoring. Many of thesesystems use a regression-based model which may be prone to underfitting whenthe model only predicts the mean of the training data. In this paper, wepresent a dynamic loss function that creates an incentive for the model topredict with the correct distribution, as well as predicting the correctvalues. Our loss function achieves this goal without sacrificing anyperformance achieving a Quadratic Weighted Kappa score of 0.752 on theAutomated Student Assessment Prize Automated Essay Scoring dataset.

The Effectiveness of a Dynamic Loss Function in Neural Network Based Automated Essay Scoring