@inproceedings{jiang-etal-2025-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task 5: Contrastive Learning for {GND} Subject Tagging with Multilingual Sentence-{BERT}",
    author = "Jiang, Hong  and
      Wang, Jin  and
      Zhang, Xuejie",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.318/",
    pages = "2443--2448",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes YNU-HPCC(Alias JH) team{'}s participation in the sub-task 2 of the SemEval-2025 Task 5, which requires fine-tuning language models to align subject tags with the TIBKAT collection. The task presents three key challenges: cross-disciplinary document coverage, bilingual (English-German) processing requirements, and extreme classification over 200,000 GND Subjects. To address these challenges, we apply a contrastive learning framework using multilingual Sentence-BERT models, implementing two innovative training strategies: mixed-negative multi-label sampling, and single-label sampling with random negative selection. Our best-performing model achieves significant improvements of 28.6{\%} in average recall, reaching 0.2252 on the core-test set and 0.1677 on the all-test set. Notably, we reveal model architecture-dependent response patterns: MiniLM-series models benefit from multi-label training (+33.5{\%} zero-shot recall), while mpnet variants excel with single-label approaches (+230.3{\%} zero-shot recall). The study further demonstrates the effectiveness of contrastive learning for multilingual semantic alignment in low-resource scenarios, providing insights for extreme classification tasks."
}