@inproceedings{wang-wang-2025-dke,
    title = "{DKE}-Research at {S}em{E}val-2025 Task 7: A Unified Multilingual Framework for Cross-Lingual and Monolingual Retrieval with Efficient Language-specific Adaptation",
    author = "Wang, Yuqi  and
      Wang, Kangshi",
    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/corrections-2025-08/2025.semeval-1.22/",
    pages = "155--159",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a unified framework for fact-checked claim retrieval, integrating contrastive learning with an in-batch multiple negative ranking loss and a conflict-aware batch sampler to enhance query-document alignment across languages. Additionally, we introduce language-specific adapters for efficient fine-tuning, enabling adaptation to previously unseen languages."
}