Automated Essay Scoring based on Two-Stage Learning

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.

7 pages, 4 figures

References in corpus (1)

• Distributed Representations of Words and Phrases and their Compositionality

Cited by in corpus (7)

• Review: Deep Learning in Electron Microscopy
• Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback
• My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism
• Play to Grade: Testing Coding Games as Classifying Markov Decision Process
• Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring
• Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling?