{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Foreign Language Faculty, Chizhou University, Chizhou, China",
    "School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, China",
    "BNRist, Tsinghua University, Beijing, China",
    "School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo, China"
  ],
  "article_type": "Research Article",
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  "author_total": 5,
  "authors": [
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        0
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      "name": "Tang He"
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    {
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      "name": "Peng Dai"
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    {
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        2
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      "name": "Rong Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Yanyang Zhao"
    },
    {
      "name": "Chen Zhao"
    }
  ],
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  "doi": "10.1371/journal.pone.0358227",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "/Social sciences/Psychology/Cognitive psychology/Language"
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      "/Social sciences/Psychology/Cognitive psychology/Language/Multilingualism"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358227&type=printable",
  "pmcid": null,
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  "published": "2026-09-11",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/55g7mttwgab10.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
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  "subjects": [
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      "origin": "plos:neuroscience",
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  "title": "Concept-aware contrastive representation learning for cross-translation semantic consistency modeling of the Analects",
  "title_text": "Concept-aware contrastive representation learning for cross-translation semantic consistency modeling of the Analects",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/55g7mttwgab10",
  "venue": "PLOS One",
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}