Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive Learning
Multi-view clustering-based multilingual data pattern mining has received significant attention in recent years due to its ability to fully leverage the complementary and consistent information from multiple languages. Although existing methods achieve encouraging performance, they often jointly opt...
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-04-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202511-28-11-0019 |
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| author | Baoying Yang |
| author_facet | Baoying Yang |
| author_sort | Baoying Yang |
| collection | DOAJ |
| description | Multi-view clustering-based multilingual data pattern mining has received significant attention in recent years due to its ability to fully leverage the complementary and consistent information from multiple languages. Although existing methods achieve encouraging performance, they often jointly optimize representation learning and pattern mining within a single feature space, which may degrade the effectiveness of multilingual data pattern mining. To address this issue, this paper proposes a multi-granularity contrastive learning-based deep multilingual data pattern mining method (MCL), which consists of three view-invariant learning modules: structure learning, semantics learning, and partitioning learning. MCL integrates these three levels of view-invariant learning into an end-to-end framework, comprehensively exploiting the consistency and complementarity of multi-view data, thereby significantly improving the accuracy and robustness of multilingual data pattern mining. Finally, through extensive experiments on five datasets, MCL shows to establish a new benchmark for ACC, NMI, and PUR, proving its superiority and effectiveness. |
| format | Article |
| id | doaj-art-05d265320f804c1f81b40b7b4fc2580f |
| institution | DOAJ |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-05d265320f804c1f81b40b7b4fc2580f2025-08-20T03:14:32ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-04-0128112291229910.6180/jase.202511_28(11).0019Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive LearningBaoying Yang0School of Foreign Languages, Zhengzhou University of Science and Technology, Zhengzhou, 450064, ChinaMulti-view clustering-based multilingual data pattern mining has received significant attention in recent years due to its ability to fully leverage the complementary and consistent information from multiple languages. Although existing methods achieve encouraging performance, they often jointly optimize representation learning and pattern mining within a single feature space, which may degrade the effectiveness of multilingual data pattern mining. To address this issue, this paper proposes a multi-granularity contrastive learning-based deep multilingual data pattern mining method (MCL), which consists of three view-invariant learning modules: structure learning, semantics learning, and partitioning learning. MCL integrates these three levels of view-invariant learning into an end-to-end framework, comprehensively exploiting the consistency and complementarity of multi-view data, thereby significantly improving the accuracy and robustness of multilingual data pattern mining. Finally, through extensive experiments on five datasets, MCL shows to establish a new benchmark for ACC, NMI, and PUR, proving its superiority and effectiveness.http://jase.tku.edu.tw/articles/jase-202511-28-11-0019multi-granularity contrastive learningtri-invariant alignmentmultilingual data mining |
| spellingShingle | Baoying Yang Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive Learning Journal of Applied Science and Engineering multi-granularity contrastive learning tri-invariant alignment multilingual data mining |
| title | Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive Learning |
| title_full | Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive Learning |
| title_fullStr | Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive Learning |
| title_full_unstemmed | Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive Learning |
| title_short | Mining Data Patterns in Chinese-English Translation via Multi-granularity Contrastive Learning |
| title_sort | mining data patterns in chinese english translation via multi granularity contrastive learning |
| topic | multi-granularity contrastive learning tri-invariant alignment multilingual data mining |
| url | http://jase.tku.edu.tw/articles/jase-202511-28-11-0019 |
| work_keys_str_mv | AT baoyingyang miningdatapatternsinchineseenglishtranslationviamultigranularitycontrastivelearning |