Article: Identification of translation bias in Chinese-Korean Confucian texts based on pre-trained language models Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.42 pp.68 - 81 Abstract: Confucian classics hold a foundational position in the history of Sino-Korean cultural exchange. However, machine translation of these texts often leads to semantic distortion and cultural bias. This paper proposes an automated bias identification framework based on the pre-trained cross-lingual model x-language model-robustly optimised bidirectional encoder representations from transformers pretraining approach. Through a multi-task architecture integrates contrastive learning, semantic role labelling, and context-aware alignment, our method effectively identifies and quantifies semantic, cultural, and grammatical deviations in translated Confucian texts. Experimental results on multiple public available corpora demonstrate that the proposed approach achieves an F1-score of 0.83 and accuracy of 85%, outperforming existing baselines in both metrics, especially in identifying culturally specific terms and nuanced expressions (F1 = 0.86 for cultural bias). This research provides valuable methodological insights for evaluating classical text translation quality and supports the accurate dissemination and digital preservation of Confucian cultural heritage. Inderscience Publishers - linking academia, business and industry through research

Title: Identification of translation bias in Chinese-Korean Confucian texts based on pre-trained language models

Authors: Zhengfeng Huang

Addresses: School of Foreign Languages, Liaoning University of International Business and Economics, Dalian, 116052, China

Abstract: Confucian classics hold a foundational position in the history of Sino-Korean cultural exchange. However, machine translation of these texts often leads to semantic distortion and cultural bias. This paper proposes an automated bias identification framework based on the pre-trained cross-lingual model x-language model-robustly optimised bidirectional encoder representations from transformers pretraining approach. Through a multi-task architecture integrates contrastive learning, semantic role labelling, and context-aware alignment, our method effectively identifies and quantifies semantic, cultural, and grammatical deviations in translated Confucian texts. Experimental results on multiple public available corpora demonstrate that the proposed approach achieves an F1-score of 0.83 and accuracy of 85%, outperforming existing baselines in both metrics, especially in identifying culturally specific terms and nuanced expressions (F1 = 0.86 for cultural bias). This research provides valuable methodological insights for evaluating classical text translation quality and supports the accurate dissemination and digital preservation of Confucian cultural heritage.

Keywords: pre-trained language models; PLMs; Chinese-Korean translation; Confucian texts; bias identification; cross-language processing.

DOI: 10.1504/IJICT.2025.150137

International Journal of Information and Communication Technology, 2025 Vol.26 No.42, pp.68 - 81

Received: 29 Aug 2025
Accepted: 26 Sep 2025
Published online: 01 Dec 2025 *

Title: Identification of translation bias in Chinese-Korean Confucian texts based on pre-trained language models

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