Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training

TL;DR

This paper introduces a rubric-specific data augmentation method for neural automated essay scoring, improving model performance by better capturing rubric items often overlooked in prior neural approaches.

Contribution

The paper proposes novel data augmentation techniques tailored to rubric items, enhancing neural models' ability to evaluate essays more comprehensively.

Findings

Achieved state-of-the-art performance on the ASAP dataset.

Demonstrated improved scoring accuracy with rubric-specific augmentation.

Enhanced model understanding of rubric criteria through augmentation.

Abstract

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.