The Application of Deep Learning in Automated Essay Evaluation

  • Shili Ge
  • Xiaoxiao Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11984)


The shift from Automated Essay Scoring (AES) to Automated Essay Evaluation (AEE) indicates that natural language processing (NLP) researchers respond positively to the request from language teaching field. Writers and teachers need more feedback about writing content and language use from AEE software beside a precise evaluative score. This requirement can be met by the neural network based deep learning technique. Deep learning has been applied in many NLP fields and great success has been made, such as machine translation, emotional analysis, question answering, and automatic summarization. Neural network based deep learning is suitable for AES research and development since AES requires mainly a precise score of writing quality. This can be accomplished with human accurately scored essays as input and scoring model as output with deep learning technology. However, AEE requires more than a score and deep learning can be used to select linguistically meaningful features for writing quality and apply in the AEE model construction. Related experiments already show the feasibility and further research is worth exploring.


Automated Essay Evaluation Automated Essay Scoring Deep learning Neural network Natural Language Processing 



This work is financially supported by the Science and Technology Project of Guangdong Province, China (2017A020220002), Graduate Education Innovation Plan of Guangdong Province (2018JGXM39) and the fund of Center for Translation Studies, Guangdong University of Foreign Studies.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shili Ge
    • 1
  • Xiaoxiao Chen
    • 1
  1. 1.Guangdong University of Foreign StudiesGuangzhouChina

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