Concept-aware contrastive representation learning for cross-translation semantic consistency modeling of the Analects
4 affiliations
- 1School of Foreign Language Faculty, Chizhou University, Chizhou, China
- 2School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, China
- 3BNRist, Tsinghua University, Beijing, China
- 4School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo, China
Abstract
As a representative Chinese classical text, the Analects has been translated into English in many versions over a long historical period, and different translators show substantial variation in concept rendering and language style. To address the lack of a quantifiable and reproducible framework for comparing semantic consistency across multiple English translations of the Analects, this paper proposes a concept-aware semantic consistency modeling framework for cross-translation comparison. The proposed method first constructs a unified parallel corpus input based on sentence groups aligned by chapter and sentence IDs. It then introduces concept-term normalization and proper-name normalization to reduce surface lexical variation across translations. In addition, it combines a shared sentence encoder, supervised contrastive learning, and concept-aware constraints to learn sentence representations with cross-translation consistency. Based on the learned representations, the framework is evaluated through two primary tasks, namely cross-translation retrieval and low-consistency detection. Experimental results show that, within the five-translation dataset examined in this study, the proposed method achieves the strongest overall performance across the evaluated settings. The Full model achieves R@1 = 0.936 and F1 = 0.807. The supplementary association analysis further shows that higher model scores tend to be associated with translation pairs labeled as high consistency. These findings support the framework as a computational aid for cross-translation comparison and for identifying translation pairs that may warrant closer expert examination, rather than as a general-purpose method for automatic translation-quality assessment.
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BibTeX
@article{he2026conceptaware,
title = {{Concept-aware contrastive representation learning for cross-translation semantic consistency modeling of the Analects}},
author = {Tang He and Peng Dai and Rong Wang and Yanyang Zhao and Chen Zhao},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358227},
url = {https://doi.org/10.1371/journal.pone.0358227},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Concept-aware contrastive representation learning for cross-translation semantic consistency modeling of the Analects
AU - Tang He
AU - Peng Dai
AU - Rong Wang
AU - Yanyang Zhao
AU - Chen Zhao
PY - 2026
DA - 2026/09/11
JO - PLOS One
DO - 10.1371/journal.pone.0358227
UR - https://doi.org/10.1371/journal.pone.0358227
AB - As a representative Chinese classical text, the Analects has been translated into English in many versions over a long historical period, and different translators show substantial variation in concept rendering and language style. To address the lack of a quantifiable and reproducible framework for comparing semantic consistency across multiple English translations of the Analects, this paper proposes a concept-aware semantic consistency modeling framework for cross-translation comparison. The proposed method first constructs a unified parallel corpus input based on sentence groups aligned by chapter and sentence IDs. It then introduces concept-term normalization and proper-name normalization to reduce surface lexical variation across translations. In addition, it combines a shared sentence encoder, supervised contrastive learning, and concept-aware constraints to learn sentence representations with cross-translation consistency. Based on the learned representations, the framework is evaluated through two primary tasks, namely cross-translation retrieval and low-consistency detection. Experimental results show that, within the five-translation dataset examined in this study, the proposed method achieves the strongest overall performance across the evaluated settings. The Full model achieves R@1 = 0.936 and F1 = 0.807. The supplementary association analysis further shows that higher model scores tend to be associated with translation pairs labeled as high consistency. These findings support the framework as a computational aid for cross-translation comparison and for identifying translation pairs that may warrant closer expert examination, rather than as a general-purpose method for automatic translation-quality assessment.
SN - 1932-6203
ER -