Style Augmented Transformer Architecture for Automatic Essay Assessment

In this paper, we present a grammar and style aware transformer-based neural network for computing the quality of a text in an automatic essay-scoring task. The proposed model takes into consideration different grammatical error categories and discourse writing styles like, concreteness, uncertainty, conviction and commitment in text along with the pre-trained language models of a text document. We have evaluated the proposed model with the automated student assessment dataset. Our preliminary investigation shows that incorporating such stylistic vectors and grammatical error categories with the BERT based language model can give us a better understanding of improving the overall evaluation of the input essays.