# The Effectiveness of a Dynamic Loss Function in Neural Network Based Automated Essay Scoring

arXiv: https://arxiv.org/abs/2305.10447 · HTML: https://mlsanity.com/paper/2305.10447/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 5 of 5)

- [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)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Automated Essay Scoring Using Transformer Models](https://mlsanity.com/paper/2110.06874/) (2021)

## Built on by (most cited first, 1 of 1)

- [Automated Feedback Generation for a Chemistry Database and Abstracting Exercise](https://mlsanity.com/paper/2305.18319/) (2023)

## Abstract (arXiv)

Neural networks and in particular the attention mechanism have brought significant advances to the field of Automated Essay Scoring. Many of these systems use a regression-based model which may be prone to underfitting when the model only predicts the mean of the training data. In this paper, we present a dynamic loss function that creates an incentive for the model to predict with the correct distribution, as well as predicting the correct values. Our loss function achieves this goal without sacrificing any performance achieving a Quadratic Weighted Kappa score of 0.752 on the Automated Student Assessment Prize Automated Essay Scoring dataset.