TY - GEN
T1 - Automatic Essay Scoring Model Based on Multi-channel CNN and LSTM
AU - Chen, Zhiyun
AU - Quan, Yinuo
AU - Qian, Dongming
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - In essay marking, manual grading will waste a lot of manpower and material resources, and the subjective judgment of marking teachers is easy to cause unfair phenomenon. Therefore, this paper proposes an automatic essay grading model combining multi-channel convolution and LSTM. The model adds a dense layer after the embedding layer, obtains the weight assignment of text through softmax function, then uses the multi-channel convolutional neural network to extract the text feature information of different granularities, and the extracted feature information is fused into the LSTM to model the text. The model is experimented on the ASAP composition data set. The experimental results show that the model proposed in this paper is 6% higher than the strong baseline model, and the automatic scoring effect is improved to a certain extent.
AB - In essay marking, manual grading will waste a lot of manpower and material resources, and the subjective judgment of marking teachers is easy to cause unfair phenomenon. Therefore, this paper proposes an automatic essay grading model combining multi-channel convolution and LSTM. The model adds a dense layer after the embedding layer, obtains the weight assignment of text through softmax function, then uses the multi-channel convolutional neural network to extract the text feature information of different granularities, and the extracted feature information is fused into the LSTM to model the text. The model is experimented on the ASAP composition data set. The experimental results show that the model proposed in this paper is 6% higher than the strong baseline model, and the automatic scoring effect is improved to a certain extent.
KW - Automatic essay scoring
KW - Long Short-Term Memory
KW - Multichannel convolution
UR - https://www.scopus.com/pages/publications/85104414305
U2 - 10.1007/978-981-16-1160-5_26
DO - 10.1007/978-981-16-1160-5_26
M3 - 会议稿件
AN - SCOPUS:85104414305
SN - 9789811611599
T3 - Communications in Computer and Information Science
SP - 337
EP - 346
BT - Intelligent Computing and Block Chain - 1st BenchCouncil International Federated Conferences, FICC 2020, Revised Selected Papers
A2 - Gao, Wanling
A2 - Hwang, Kai
A2 - Wang , Changyun
A2 - Li, Weiping
A2 - Qiu, Zhigang
A2 - Wang, Lei
A2 - Zhou, Aoying
A2 - Qian, Weining
A2 - Jin, Cheqing
A2 - Zhang, Zhifei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020
Y2 - 30 October 2020 through 3 November 2020
ER -