基于孪生网络和BERT模型的主观题自动评分系统

由于现在缺乏多语言教学中的主观题自动评分, 针对这一问题提出了一种基于孪生网络和BERT模型的主观题自动评分系统. 主观题的问题文本和答案文本通过自然语言预处理BERT模型得到文本的句向量, BERT模型已经在大规模多种语言的语料上经过训练, 得到的文本向量包含了丰富的上下文语义信息, 并且能处理多种语言信息. 然后把...

Automatic Short Answer Grading Based on Siamese Network and BERT Model

To make up the gap of short answer grading systems in multilingual teaching, this paper proposes an automatic short answer grading system based on siamese network and bidirectional encoder representations from transformers (BERT) model. First, the question and answer texts of short answers yield sentence vectors of texts with the natural language-preprocessed BERT model. The BERT model has been trained on a large-scale multilingual corpus, and the obtained text vectors contain rich contextual semantic information and can deal with multilingual information. Then, the sentence vectors of question and answer texts are subjected to the calculation of the semantic similarity in the siamese network of a deep network. Finally, a logistic regression classifier is employed to complete automatic short answer grading. The datasets used for automatic short answer grading tasks are provided by the Hewlett Foundation, and the quadratic weighted kappa coefficient is used as the evaluation index of the model. The experimental results show that the proposed method outperforms other baseline models for automatic short answer grading in each data subset.