Journal of Computational Intelligence Theory and Its Applications

Authors: Steve Thomas, Steephen Joseph, Basil Sunny, Surekha Mariam Varghese, Aby Abahai T

Abstract: Traditional automated essay scoring systems rely on carefully designed features to evaluate and score essays. The performance of such systems is tightly bound to the quality of the underlying features. However, it is difficult to manually design the most informative features for such a system. In this paper, we develop an approach based on recurrent neural networks to learn the relation between an essay and its assigned score, without any feature engineering. We use Long Short-Term Memory neural network model for the task of automated essay scoring and perform some analysis to get some insights of the model. The results show that our system, which is based on long short-term memory networks, outperforms a strong baseline by 5.6% in terms of quadratic weighted Kappa, without requiring any feature engineering.