Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Liu, Jiawei, Xu, Yang, Zhu, Yaguang
Institution: University of Science and Technology of China
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2913657017