【摘要】 作文自动评分技术是一种利用机器学习进行自然语言处理的技术。目前，基于深度学习的端到端模型在作文自动评分领域已经广泛使用。然而，由于端到端模型难以获取不同特征之间的相关性，因此提出一种基于语义特征融合的作文自动评分方法（TSEF）。该方法主要分为特征提取和特征融合2个阶段。特征提取阶段，使用Bert模型对输入文本进行预训练，并使用多头注意力机制对输入文本进行自训练，以补充预训练的不足；特征融合阶段，使用交叉融合方法将获取的不同特征融合，以此获得更好性能的模型。在实验中，将TSEF与许多强基线进行比较，结果表明了本文方法的有效性和稳健性。更多还原

【Abstract】 Automatic composition scoring technology is a kind of natural language processing technology using machine learning.At present,end-to-end models based on deep learning have been widely used in the field of automatic essay scoring. However,because of the difficulty in obtaining correlations between different features in end-to-end models,Automatic Scoring Method for Composition Based on Semantic Feature Fusion(TSEF)has been proposed. This method is mainly divided into two stages: feature extraction and feature fusion. In the feature extraction stage,the Bert model is used to pre-train the input text,and a multihead-attention mechanism is used to self-train the input text to supplement the shortcomings of pre-training; In the feature fusion stage,cross fusion methods are used to fuse the different features obtained in order to obtain a better performance model. In the experiment,TSEF was compared with many strong baselines,and the results demonstrated the effectiveness and robustness of our method.更多还原