@inproceedings{huang-etal-2025-multilingual,
    title = "Multilingual Generative Retrieval via Cross-lingual Semantic Compression",
    author = "Huang, Yuxin  and
      Wu, Simeng  and
      Song, Ran  and
      Xiang, Yan  and
      Xian, Yantuan  and
      Gao, Shengxiang  and
      Yu, Zhengtao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.575/",
    doi = "10.18653/v1/2025.findings-emnlp.575",
    pages = "10855--10866",
    ISBN = "979-8-89176-335-7",
    abstract = "Generative Information Retrieval is an emerging retrieval paradigm that exhibits remarkable performance in monolingual scenarios. However, applying these methods to multilingual retrieval still encounters two primary challenges, cross-lingual identifier misalignment and identifier inflation. To address these limitations, we propose Multilingual Generative Retrieval via Cross-lingual Semantic Compression (MGR-CSC), a novel framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space, and we propose a dynamic multi-step constrained decoding strategy during retrieval. MGR-CSC improves cross-lingual alignment by assigning consistent identifiers and enhances decoding efficiency by reducing redundancy. Experiments demonstrate that MGR-CSC achieves outstanding retrieval accuracy, improving by 6.83{\%} on mMarco100k and 4.77{\%} on mNQ320k, while reducing document identifiers length by 74.51{\%} and 78.2{\%}, respectively. We publicly release our dataset and code at https://github.com/simengggg/MGR-CSC"
}