Automated Scoring of Translations with BERT Models: Chinese and English Language Case Study

被引:0
|
作者
Cui, Yizhuo [1 ]
Liang, Maocheng [2 ]
机构
[1] North China Univ Technol, Sch Humanities & Law, Beijing 100144, Peoples R China
[2] Beihang Univ, Sch Foreign Languages, Beijing 100191, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 05期
关键词
large language model; BERT; automated scoring of translations; large-scale translation contest;
D O I
10.3390/app14051925
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can reliably assess translation quality and identify high-quality translations for potential recognition. It takes the Han Suyin International Translation Contest as a case study, which is a large-scale and influential translation contest in China, with a history of over 30 years. The experimental results show that the BERT-assist system is a reliable second rater for massive translations in terms of translation quality, as it can effectively sift out high-quality translations with a reliability of r = 0.9 or higher. Thus, the automated translation scoring system based on BERT can satisfactorily predict the ranking of translations according to translation quality and sift out high-quality translations potentially shortlisted for prizes.
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页数:17
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