# Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores

arXiv: https://arxiv.org/abs/2203.00354 · HTML: https://mlsanity.com/paper/2203.00354/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://mlsanity.com/paper/1804.09541/) (2018)
- [Automatic Text Scoring Using Neural Networks](https://mlsanity.com/paper/1606.04289/) (2016)
- [Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs](https://mlsanity.com/paper/1812.04718/) (2018)
- [Automated Essay Scoring based on Two-Stage Learning](https://mlsanity.com/paper/1901.07744/) (2019)
- [Text Data Augmentation: Towards better detection of spear-phishing emails](https://mlsanity.com/paper/2007.02033/) (2020)

## Built on by (most cited first, 3 of 3)

- [Data Augmentation for Automated Essay Scoring using Transformer Models](https://mlsanity.com/paper/2210.12809/) (2022)
- [Phrase-Level Adversarial Training for Mitigating Bias in Neural Network-based Automatic Essay Scoring](https://mlsanity.com/paper/2409.04795/) (2024)
- [Calibrating Generative AI to Produce Realistic Essays for Data Augmentation](https://mlsanity.com/paper/2602.06772/) (2026)

## Abstract (arXiv)

Automated essay scoring plays an important role in judging students' language abilities in education. Traditional approaches use handcrafted features to score and are time-consuming and complicated. Recently, neural network approaches have improved performance without any feature engineering. Unlike other natural language processing tasks, only a small number of datasets are publicly available for automated essay scoring, and the size of the dataset is not sufficiently large. Considering that the performance of a neural network is closely related to the size of the dataset, the lack of data limits the performance improvement of the automated essay scoring model. In this paper, we proposed a method to increase the number of essay-score pairs using back-translation and score adjustment and applied it to the Automated Student Assessment Prize dataset for augmentation. We evaluated the effectiveness of the augmented data using models from prior work. In addition, performance was evaluated in a model using long short-term memory, which is widely used for automated essay scoring. The performance of the models was improved by using augmented data to train the models.