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作 者:卢鹭 郑伊 方一新[1] Lu Lu;Zheng Yi;Fang Yixin(Center for Studies of History of Chinese Language,Zhejiang University,Hangzhou 310058,China;Research Institute for Ancient Books,Zhejiang University,Hangzhou 310058,China)
机构地区:[1]浙江大学汉语史研究中心,浙江杭州310058 [2]浙江大学古籍研究所,浙江杭州310058
出 处:《浙江大学学报(人文社会科学版)》2025年第2期82-101,共20页Journal of Zhejiang University:Humanities and Social Sciences
基 金:国家社科基金青年项目(20CYY022)。
摘 要:考辨古代文献作(译)者的研究往往需要根据已知文本的特点去推断未知文本。近数十年来,早期汉译佛经的译者和时代题署问题受到了学界的普遍关注,研究者通常需要综合文献和语言层面的证据进行交叉验证,但就已有研究成果而言,所提取的语言层面的鉴别标准往往不够充分,在对证据的解读上仍缺乏科学性。使用语言模型提取译经文本的特征信息,进而使用分类器(全连接层)进行分类,是一种基于深度学习模型的考辨方法和思路。以安世高译经考辨为例,实验表明,借助语言模型能够有效区分安世高译经和非安世高译经。未来在使用语言模型进行译经考辨时,应当重视语言风格的倾向性,而不局限于独特性,同时关注译经内容形式对检测结果的潜在影响。The study of identifying the authors or translators of ancient texts often requires inferring unknown texts based on the characteristics of known ones.In recent decades,the issues of translators and the dating of early Chinese Buddhist translations have attracted widespread attention in the academic community.This paper utilizes deep learning models,specifically BERT and RBT6,to extract feature information from translated texts and conduct a comprehensive examination of the inscription issues in An Shigao’s translations.Additionally,the study validates the effectiveness of language models in identifying texts in Chinese Buddhist translations.The study uses 14 widely accepted translations by An Shigao as positive samples and randomly selects non-An Shigao translations as negative samples.The experimental results show that the RBT6 model outperforms both the BERT and traditional support vector machine(SVM)models in precision,recalls,and other metrics,demonstrating superior classification performance.As a validation,the trained model is applied to evaluate 35 translations attributed to An Shigao but widely regarded as unreliable by scholars.The model’s evaluations are found to align perfectly with the conclusions established through textual criticism,thereby confirming its effectiveness in distinguishing authentic translations.Additionally,to examine whether factors such as variant texts,punctuation segmentation,and text length affect the detection results,the study employs techniques like masking,random punctuation insertion,and random segment extraction on the same set of texts.The results of both experiments are consistent,confirming that these factors had no significant effect on the model’s detection outcomes.This study applies the three trained models to detect the disputed or newly discovered translations attributed to An Shigao.The models identify the following texts as translations by An Shigao:T101 Za ahan jing杂阿含经(excluding sutras 9 and 10),T1557 Apitan wufaxing jing阿毗昙五法行经,T73
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