@inproceedings{zhang-etal-2025-zero,
    title = "Zero-shot Cross-lingual {NER} via Mitigating Language Difference: An Entity-aligned Translation Perspective",
    author = "Zhang, Zhihao  and
      Lee, Sophia Yat Mei  and
      Zhang, Dong  and
      Li, Shoushan  and
      Zhou, Guodong",
    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.244/",
    doi = "10.18653/v1/2025.findings-emnlp.244",
    pages = "4541--4557",
    ISBN = "979-8-89176-335-7",
    abstract = "Cross-lingual Named Entity Recognition (CL-NER) aims to transfer knowledge from high-resource languages to low-resource languages. However, existing zero-shot CL-NER (ZCL-NER) approaches primarily focus on Latin script language (LSL), where shared linguistic features facilitate effective knowledge transfer. In contrast, for non-Latin script language (NSL), such as Chinese and Japanese, performance often degrades due to deep structural differences. To address these challenges, we propose an entity-aligned translation (EAT) approach. Leveraging large language models (LLMs), EAT employs a dual-translation strategy to align entities between NSL and English. In addition, we fine-tune LLMs using multilingual Wikipedia data to enhance the entity alignment from source to target languages."
}