计算机与现代化Issue(6)：8-13,24,7.DOI:10.3969/j.issn.1006-2475.2024.06.002
基于语义特征融合的作文自动评分方法
Automatic Scoring Method for Composition Based on Semantic Feature Fusion
摘要
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: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.关键词
作文自动评分/自训练/预训练/交叉融合Key words
automatic grading of essays/self-training/pre-training/cross fusion分类
信息技术与安全科学引用本文复制引用
袁航,杨勇,任鸽,帕力旦·吐尔逊..基于语义特征融合的作文自动评分方法[J].计算机与现代化,2024,(6):8-13,24,7.基金项目
新疆维吾尔自治区自然科学基金项目(2021D01B72) （2021D01B72）
国家自然科学基金资助项目(62167008,62066044) （62167008,62066044）
