Automated Essay Scoring: A Siamese Bidirectional LSTM Neural Network Architecture

Symmetry

Guoxi Liang¹  GyuSang Choi²  Dongwon Jeong³  Byung-Won On³  Hyun-Chul Kim⁴ 

[1] Department of Global Entrepreneurship, Kunsan National University, Gunsan 54150, Korea;Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea;Department of Software Convergence Engineering, Kunsan National University, Gunsan 54150, Korea;Department of Technological Business Startup, Kunsan National University, Gunsan 54150, Korea;

关键词: automated essay scoring (AES);    deep learning;    neural network;    long short-term memory;    essay;    rating criteria;   

DOI  :  10.3390/sym10120682

来源: DOAJ

【 摘 要 】

Essay scoring is a critical task in education. Implementing automated essay scoring (AES) helps reduce manual workload and speed up learning feedback. Recently, neural network models have been applied to the task of AES and demonstrates tremendous potential. However, the existing work only considered the essay itself without considering the rating criteria behind the essay. One of the reasons is that the various kinds of rating criteria are very hard to represent. In this paper, we represent rating criteria by some sample essays that were provided by domain experts and defined a new input pair consisting of an essay and a sample essay. Corresponding to this new input pair, we proposed a symmetrical neural network AES model that can accept the input pair. The model termed Siamese Bidirectional Long Short-Term Memory Architecture (SBLSTMA) can capture not only the semantic features in the essay but also the rating criteria information behind the essays. We use the SBLSTMA model for the task of AES and take the Automated Student Assessment Prize (ASAP) dataset as evaluation. Experimental results show that our approach is better than the previous neural network methods.

【 授权许可】

Unknown