# Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

arXiv: https://arxiv.org/abs/1804.06898 · HTML: https://mlsanity.com/paper/1804.06898/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 16 of 16)

- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
- [On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation](https://mlsanity.com/paper/2205.03835/) (2022)
- [Automated essay scoring using efficient transformer-based language models](https://mlsanity.com/paper/2102.13136/) (2021)
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- [A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality](https://mlsanity.com/paper/2404.02529/) (2024)
- [Transformer Models for Text Coherence Assessment](https://mlsanity.com/paper/2109.02176/) (2021)
- [Deep Learning Architecture for Automatic Essay Scoring](https://mlsanity.com/paper/2206.08232/) (2022)
- [The effects of data size on Automated Essay Scoring engines](https://mlsanity.com/paper/2108.13275/) (2021)
- [Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring](https://mlsanity.com/paper/2211.13373/) (2022)
- [Comparison Study Between Token Classification and Sequence Classification In Text Classification](https://mlsanity.com/paper/2211.13899/) (2022)
- [Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training](https://mlsanity.com/paper/2309.02740/) (2023)
- [Composable Cross-prompt Essay Scoring by Merging Models](https://mlsanity.com/paper/2505.18548/) (2025)
- [Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests](https://mlsanity.com/paper/2506.20119/) (2025)
- [Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers](https://mlsanity.com/paper/2607.16085/) (2026)

## Abstract (arXiv)

We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.