# Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training

arXiv: https://arxiv.org/abs/2309.02740 · HTML: https://mlsanity.com/paper/2309.02740/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models](https://mlsanity.com/paper/1808.01688/) (2018)
- [Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?](https://mlsanity.com/paper/2012.15180/) (2020)
- [Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input](https://mlsanity.com/paper/1804.06898/) (2018)
- [Automated Essay Scoring based on Two-Stage Learning](https://mlsanity.com/paper/1901.07744/) (2019)
- [Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour](https://mlsanity.com/paper/2005.12078/) (2020)
- [Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems](https://mlsanity.com/paper/2007.06796/) (2020)

## Built on by (most cited first, 1 of 1)

- [Exploring the Utilities of the Rationales from Large Language Models to Enhance Automated Essay Scoring](https://mlsanity.com/paper/2510.27131/) (2025)

## Abstract (arXiv)

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.