Rubric-Specific Approach to Automated Essay Scoring with Augmentation
  Training
Brian Cho Youngbin Jang Jaewoong Yoon
Neural based approaches to automatic evaluation of subjective responses have
shown superior performance and efficiency compared to traditional rule-based
and feature engineering oriented solutions. However, it remains unclear whether
the suggested neural solutions are sufficient replacements of human raters as
we find recent works do not properly account for rubric items that are
essential for automated essay scoring during model training and validation. In
this paper, we propose a series of data augmentation operations that train and
test an automated scoring model to learn features and functions overlooked by
previous works while still achieving state-of-the-art performance in the
Automated Student Assessment Prize dataset.