# Hey AI Can You Grade My Essay?: Automatic Essay Grading

arXiv: https://arxiv.org/abs/2410.09319 · HTML: https://mlsanity.com/paper/2410.09319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Automatic Text Scoring Using Neural Networks](https://mlsanity.com/paper/1606.04289/) (2016)

## Abstract (arXiv)

Automatic essay grading (AEG) has attracted the the attention of the NLP community because of its applications to several educational applications, such as scoring essays, short answers, etc. AEG systems can save significant time and money when grading essays. In the existing works, the essays are graded where a single network is responsible for the whole process, which may be ineffective because a single network may not be able to learn all the features of a human-written essay. In this work, we have introduced a new model that outperforms the state-of-the-art models in the field of AEG. We have used the concept of collaborative and transfer learning, where one network will be responsible for checking the grammatical and structural features of the sentences of an essay while another network is responsible for scoring the overall idea present in the essay. These learnings are transferred to another network to score the essay. We also compared the performances of the different models mentioned in our work, and our proposed model has shown the highest accuracy of 85.50%.