期刊
SYMMETRY-BASEL
卷 10, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/sym10120682
关键词
automated essay scoring (AES); deep learning; neural network; long short-term memory; essay; rating criteria
资金
- Wenzhou Public Technology Planning Program [S20160018]
- National Research Foundation of Korea Grant - Korean Government [NRF-2016R1A2B1014843, NRF-2017M3C4A7068188]
- Ministry of Trade, Industry and Energy (MOTIE, Sejong, Korea) under the Industrial Technology Innovation Program [10063130]
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2016R1A2B4007498]
- MSIP (Ministry of Science, ICT and Future Planning), Gwacheon, Korea, under the ITRC (Information Technology Research Center) support program [IITP-2018-2016-0-00313]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2016-0-00313-003] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Korea Evaluation Institute of Industrial Technology (KEIT) [10063130] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2016R1A2B4007498, 2016R1A2B1014843, 2017M3C4A7068188] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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