# Joint Multi-Domain Learning for Automatic Short Answer Grading

arXiv: https://arxiv.org/abs/1902.09183 · HTML: https://mlsanity.com/paper/1902.09183/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [A large language model-assisted education tool to provide feedback on open-ended responses](https://mlsanity.com/paper/2308.02439/) (2023)
- [Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers](https://mlsanity.com/paper/2204.03503/) (2022)
- [Reducing the Cost: Cross-Prompt Pre-Finetuning for Short Answer Scoring](https://mlsanity.com/paper/2408.13966/) (2024)
- [Directed Graph-alignment Approach for Identification of Gaps in Short Answers](https://mlsanity.com/paper/2504.04473/) (2025)

## Abstract (arXiv)

One of the fundamental challenges towards building any intelligent tutoring system is its ability to automatically grade short student answers. A typical automatic short answer grading system (ASAG) grades student answers across multiple domains (or subjects). Grading student answers requires building a supervised machine learning model that evaluates the similarity of the student answer with the reference answer(s). We observe that unlike typical textual similarity or entailment tasks, the notion of similarity is not universal here. On one hand, para-phrasal constructs of the language can indicate similarity independent of the domain. On the other hand, two words, or phrases, that are not strict synonyms of each other, might mean the same in certain domains. Building on this observation, we propose JMD-ASAG, the first joint multidomain deep learning architecture for automatic short answer grading that performs domain adaptation by learning generic and domain-specific aspects from the limited domain-wise training data. JMD-ASAG not only learns the domain-specific characteristics but also overcomes the dependence on a large corpus by learning the generic characteristics from the task-specific data itself. On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus. Further, on the benchmarking dataset, we report state-of-the-art results against all existing non-neural and neural models.