@article{xu-etal-2025-taxopro,
    title = "{T}axo{P}ro: A Plug-In {L}o{RA}-based Cross-Domain Method for Low-Resource Taxonomy Completion",
    author = "Xu, Hongyuan  and
      Niu, Yuhang  and
      Liu, Ciyi  and
      Wen, Yanlong  and
      Yuan, Xiaojie",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "13",
    year = "2025",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/corrections-2025-07/2025.tacl-1.27/",
    doi = "10.1162/tacl_a_00755",
    pages = "557--576",
    abstract = "Low-resource taxonomy completion aims to automatically insert new concepts into the existing taxonomy, in which only a few in-domain training samples are available. Recent studies have achieved considerable progress by incorporating prior knowledge from pre-trained language models (PLMs). However, these studies tend to overly rely on such knowledge and neglect the shareable knowledge across different taxonomies. In this paper, we propose TaxoPro, a plug-in LoRA-based cross-domain method, that captures shareable knowledge from the high- resource taxonomy to improve PLM-based low-resource taxonomy completion techniques. To prevent negative interference between domain-specific and domain-shared knowledge, TaxoPro decomposes cross- domain knowledge into domain-shared and domain-specific components, storing them using low-rank matrices (LoRA). Additionally, TaxoPro employs two auxiliary losses to regulate the flow of shareable knowledge. Experimental results demonstrate that TaxoPro improves PLM-based techniques, achieving state-of-the-art performance in completing low-resource taxonomies. Code is available at https://github.com/cyclexu/TaxoPro."
}