Qi FangThis email address is being protected from spambots. You need JavaScript enabled to view it.
School of Foreign Languages, Zhengzhou University of Science and Technology, Zhengzhou, 450064, China
Received: September 29, 2024 Accepted: April 4, 2025 Publication Date: June 15, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Bilingual terminology alignment databases represent crucial resources in the field of natural language processing, holding significant value for multilingual applications such as cross-lingual information retrieval and machine translation. Bilingual terminology pairs are typically obtained through manual translation or automatic extraction from bilingual parallel corpora. However, manual translation requires domain-specific expertise and proves time-consuming and labor-intensive, while large-scale bilingual parallel corpora in specific domains remain scarce. To this end, a terminology alignment method based on multi-level feature fusion (TA-MFF) is proposed for Japanese scientific and technological literature terminology translation. First, a multi-engine collaborative generation mechanism is designed to produce target pseudo terminology candidates through parallel translations from heterogeneous machine translation systems, effectively expanding the coverage of potential translations while mitigating single-engine bias. Second, a hybrid feature extraction architecture is constructed by integrating Transformer’s multi-head attention with BiLSTM’s sequential modeling capabilities, where positional encoding is deliberately omitted to leverage BiLSTM’s inherent strength in capturing positional relationships, thereby enhancing context-aware feature representation. Third, an adaptive multi-level fusion strategy is developed through the synergistic combination of soft attention-based global interaction features and cosine similarity-based local interaction features, with trainable weights automatically balancing their respective contributions to achieve comprehensive semantic modeling. These innovations collectively address the critical challenges of translation ambiguity reduction, cross-lingual feature alignment, and multi-perspective similarity evaluation in Japanese scientific terminology alignment, ultimately improving both precision and robustness compared to conventional approaches.
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