# Automated Essay Scoring based on Two-Stage Learning

arXiv: https://arxiv.org/abs/1901.07744 · HTML: https://mlsanity.com/paper/1901.07744/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 12 of 12)

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- [Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores](https://mlsanity.com/paper/2203.00354/) (2022)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
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- [Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring](https://mlsanity.com/paper/2211.13373/) (2022)
- [Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training](https://mlsanity.com/paper/2309.02740/) (2023)
- [Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring](https://mlsanity.com/paper/2502.08450/) (2025)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Empirical Comparison of Encoder-Based Language Models and Feature-Based Supervised Machine Learning Approaches to Automated Scoring of Long Essays](https://mlsanity.com/paper/2601.02659/) (2026)

## Abstract (arXiv)

Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e.g. the essays composed of permuted sentences and the prompt-irrelevant essays. Focusing on the problem, we develop a Two-Stage Learning Framework (TSLF) which integrates the advantages of both feature-engineered and end-to-end AES models. In experiments, we compare TSLF against a number of strong baselines, and the results demonstrate the effectiveness and robustness of our models. TSLF surpasses all the baselines on five-eighths of prompts and achieves new state-of-the-art average performance when without negative samples. After adding some adversarial essays to the original datasets, TSLF outperforms the feature-engineered and end-to-end baselines to a great extent, and shows great robustness.