Data Augmentation for Automated Essay Scoring using Transformer Models

TL;DR

This paper explores the use of transformer models combined with data augmentation techniques to improve automated essay scoring, demonstrating their effectiveness across multiple topics.

Contribution

It introduces the application of transformer models with data augmentation for automated essay scoring, showing their advantages over previous RNN-based approaches.

Findings

Transformer models outperform RNNs and LSTMs in essay scoring accuracy.

Data augmentation enhances model robustness and generalization.

A single transformer-based model effectively scores essays across various topics.

Abstract

Automated essay scoring is one of the most important problem in Natural Language Processing. It has been explored for a number of years, and it remains partially solved. In addition to its economic and educational usefulness, it presents research problems. Transfer learning has proved to be beneficial in NLP. Data augmentation techniques have also helped build state-of-the-art models for automated essay scoring. Many works in the past have attempted to solve this problem by using RNNs, LSTMs, etc. This work examines the transformer models like BERT, RoBERTa, etc. We empirically demonstrate the effectiveness of transformer models and data augmentation for automated essay grading across many topics using a single model.