Automated Essay Scoring using Generative Adversarial Network
Traditional automated essay scoring methods heavily rely on feature engineering to evaluate and assign scores to essays,which means the quality of the selected features have huge impacts on the performance of such methods.In addition,it also costs a number of labors to manually design the most informative features and requires experts knowledge at most of the time.In this paper,we innovatively propose an adversarial process for learning the relation between an essay and its assigned score,in which we simultaneously train a generative model G and a discriminative model D attempts to distinguish the generated score from the ground truth score without any feature engineering.The experimental results show our approach outperforms several state-of-the-art methods.
Automated Essay Scoring Generative Adversarial Network Natural Language Processing
Jia Zhu Jiaqi Lun Xuming Wang Min Yang Changqin Huang Gabriel Pui Cheong Fung
School of Computer Science,South China Normal University Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences Department of Systems Engineering and Engineering Management,The Chinese University of Hong Kong
国内会议
广州
英文
904-905
2018-05-26（万方平台首次上网日期，不代表论文的发表时间）