基于深度学习的主观题自动评分效度研究——以大学校本英语水平考试汉译英试题为例  

Validation of a deep learning based automatic scoring engine for constructed responses:A case study of the Chinese-English translation in SJTU English Proficiency Test

作　　者：张利东 朱一清[1] ZHANG Lidong;ZHU Yiqing

机构地区：[1]上海交通大学,上海200240

出　　处：《外语界》2022年第2期41-48,55,共9页Foreign Language World

基　　金：上海市浦江人才计划(编号15PJC072)的资助。

摘　　要：本研究以上海交通大学英语水平考试的汉译英试题为例,探讨了AI自动评分原理与机制,并基于大规模考试数据验证其评分效度。研究发现,AI自动评分结果与人工评分相关系数可达0.76,两种评分结果的均值无显著性差异,但在高分段和低分段人工评分的质量更高。本研究还对AI自动评分在大规模考试中应用的可行性以及目前存在的若干问题进行了探讨。This study provided an explication of the working mechanism of an AI based automatic scoring engine for Chinese-English translation of SJTU-EPT,and evaluated the validity of automatic scoring using large-scale test data.The results showed that the correlation coefficient between automatic scores and human scores was as high as 0.76,and there was no significant difference between the mean scores generated by the two scoring methods.Human scoring,however,slightly outperformed automatic scoring in grading translations of high and low quality.Discussions were presented on practicality of employing automatic scoring in large-scale tests and current problems associated with the performance of AI based automatic scoring.

关 键 词：深度学习 自动评分 效度验证 汉译英 

分 类 号：H319[语言文字—英语]