Automatic Scoring Method for Composition Based on Semantic Feature Fusion
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:fea-ture extraction and feature fusion.In the feature extraction stage,the Bert model is used to pre-train the input text,and a multi-head-attention mechanism is used to self-train the input text to supplement the shortcomings of pre-training;In the feature fu-sion 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.
automatic grading of essaysself-trainingpre-trainingcross fusion
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