# Automatic Short Answer Grading via Multiway Attention Networks

arXiv: https://arxiv.org/abs/1909.10166 · HTML: https://mlsanity.com/paper/1909.10166/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers](https://mlsanity.com/paper/2204.03503/) (2022)
- [Content Knowledge Identification with Multi-Agent Large Language Models (LLMs)](https://mlsanity.com/paper/2404.07960/) (2024)
- [ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation](https://mlsanity.com/paper/2601.09703/) (2026)
- [Finetuning Transformer Models to Build ASAG System](https://mlsanity.com/paper/2109.12300/) (2021)
- [Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading](https://mlsanity.com/paper/2508.04063/) (2025)
- [Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests](https://mlsanity.com/paper/2506.20119/) (2025)
- [AI-Enabled grading with near-domain data for scaling feedback with human-level accuracy](https://mlsanity.com/paper/2512.04113/) (2025)

## Abstract (arXiv)

Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads. However, ASAG is a very challenging task due to two reasons: (1) student answers are made up of free text which requires a deep semantic understanding; and (2) the questions are usually open-ended and across many domains in K-12 scenarios. In this paper, we propose a generalized end-to-end ASAG learning framework which aims to (1) autonomously extract linguistic information from both student and reference answers; and (2) accurately model the semantic relations between free-text student and reference answers in open-ended domain. The proposed ASAG model is evaluated on a large real-world K-12 dataset and can outperform the state-of-the-art baselines in terms of various evaluation metrics.