@inproceedings{huang-etal-2025-boosting,
    title = "Boosting Data Utilization for Multilingual Dense Retrieval",
    author = "Huang, Chao  and
      Mo, Fengran  and
      Chen, Yufeng  and
      Guan, Changhao  and
      Yue, Zhenrui  and
      Wang, Xinyu  and
      Xu, Jinan  and
      Huang, Kaiyu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.624/",
    pages = "12373--12389",
    ISBN = "979-8-89176-332-6",
    abstract = "Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model architecture, we propose a method to boost data utilization for multilingual dense retrieval by obtaining high-quality hard negative samples and effective mini-batch data. The extensive experimental results on a multilingual retrieval benchmark, MIRACL, with 16 languages demonstrate the effectiveness of our method by outperforming several existing strong baselines."
}