Automated Essay Scoring based on Two-Stage Learning

TL;DR

This paper introduces a Two-Stage Learning Framework for Automated Essay Scoring that effectively detects adversarial samples and outperforms existing methods in robustness and accuracy across multiple prompts.

Contribution

The paper proposes a novel Two-Stage Learning Framework combining feature-engineered and end-to-end models, enhancing adversarial robustness and scoring accuracy in AES.

Findings

TSLF outperforms baselines on most prompts.

Achieves state-of-the-art average performance without negative samples.

Demonstrates robustness against adversarial essays.

Abstract

Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e.g. the essays composed of permuted sentences and the prompt-irrelevant essays. Focusing on the problem, we develop a Two-Stage Learning Framework (TSLF) which integrates the advantages of both feature-engineered and end-to-end AES models. In experiments, we compare TSLF against a number of strong baselines, and the results demonstrate the effectiveness and robustness of our models. TSLF surpasses all the baselines on five-eighths of prompts and achieves new state-of-the-art average performance when without negative samples. After adding some adversarial essays to the original datasets, TSLF outperforms the feature-engineered and end-to-end baselines to a great extent, and shows great robustness.