  This paper presents methods for improving automated essay scoring with
techniques that address the computational trade-offs of self-attention and
document length. To make Automated Essay Scoring (AES) more useful to
practitioners, researchers must overcome the challenges of data and label
availability, authentic and extended writing, domain scoring, prompt and source
variety, and transfer learning. This paper addresses these challenges using
neural network models by employing techniques that preserve essay length as an
important feature without increasing model training costs. It introduces
techniques for minimizing classification loss on ordinal labels using
multi-objective learning, capturing semantic information across the entire
essay using sentence embeddings to use transformer architecture across
arbitrarily long documents, the use of such models for transfer learning,
automated hyperparameter generation based on prompt-corpus metadata, and, most
importantly, the use of semantic information to provide meaningful insights
into student reading through analysis of passage-dependent writing resulting in
state-of-the-art results for various essay tasks.
