# Automatic Text Scoring Using Neural Networks

arXiv: https://arxiv.org/abs/1606.04289 · HTML: https://mlsanity.com/paper/1606.04289/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2016.

## Built on by (most cited first, 30 of 51)

- [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)
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- [When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs](https://mlsanity.com/paper/2309.14488/) (2023)
- [Automated essay scoring using efficient transformer-based language models](https://mlsanity.com/paper/2102.13136/) (2021)
- [A Survey on Using Gaze Behaviour for Natural Language Processing](https://mlsanity.com/paper/2112.15471/) (2021)
- [LongWanjuan: Towards Systematic Measurement for Long Text Quality](https://mlsanity.com/paper/2402.13583/) (2024)
- [LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing](https://mlsanity.com/paper/2502.11368/) (2025)
- [Short-answer scoring with ensembles of pretrained language models](https://mlsanity.com/paper/2202.11558/) (2022)
- [Empirical Study of Large Language Models as Automated Essay Scoring Tools in English Composition__Taking TOEFL Independent Writing Task for Example](https://mlsanity.com/paper/2401.03401/) (2024)
- [Unveiling the Tapestry of Automated Essay Scoring: A Comprehensive Investigation of Accuracy, Fairness, and Generalizability](https://mlsanity.com/paper/2401.05655/) (2024)
- [Two Heads Are Better Than One: Dual-Model Verbal Reflection at Inference-Time](https://mlsanity.com/paper/2502.19230/) (2025)
- [LCES: Zero-shot Automated Essay Scoring via Pairwise Comparisons Using Large Language Models](https://mlsanity.com/paper/2505.08498/) (2025)
- [LLMs in Education: Novel Perspectives, Challenges, and Opportunities](https://mlsanity.com/paper/2409.11917/) (2024)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling?](https://mlsanity.com/paper/2102.12971/) (2021)
- [Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset](https://mlsanity.com/paper/2310.18373/) (2023)
- [Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores](https://mlsanity.com/paper/2203.00354/) (2022)
- [Deep Learning Architecture for Automatic Essay Scoring](https://mlsanity.com/paper/2206.08232/) (2022)
- [H-AES: Towards Automated Essay Scoring for Hindi](https://mlsanity.com/paper/2302.14635/) (2023)
- [Unveiling the Sentinels: Assessing AI Performance in Cybersecurity Peer Review](https://mlsanity.com/paper/2309.05457/) (2023)
- [EXPATS: A Toolkit for Explainable Automated Text Scoring](https://mlsanity.com/paper/2104.03364/) (2021)
- [LADDER: Latent Boundary-guided Adversarial Training](https://mlsanity.com/paper/2206.03717/) (2022)
- [Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations](https://mlsanity.com/paper/2302.10479/) (2023)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [EssayCBM: Rubric-Aligned Concept Bottleneck Models for Transparent Essay Grading](https://mlsanity.com/paper/2512.20817/) (2025)
- [Toward Educator-focused Automated Scoring Systems for Reading and Writing](https://mlsanity.com/paper/2112.11973/) (2021)

## Abstract (arXiv)

Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the predictive features of the system need to be manually engineered by human experts. We introduce a model that forms word representations by learning the extent to which specific words contribute to the text's score. Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches. In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model has found more discriminative.