@inproceedings{wang-etal-2025-neighxlm,
    title = "{N}eigh{XLM}: Enhancing Cross-Lingual Transfer in Low-Resource Languages via Neighbor-Augmented Contrastive Pretraining",
    author = "Wang, Sicheng  and
      Wu, Wenyi  and
      Zhang, Zibo",
    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/author-page-yu-wang-polytechnic/2025.findings-emnlp.163/",
    doi = "10.18653/v1/2025.findings-emnlp.163",
    pages = "3019--3030",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent progress in multilingual pretraining has yielded strong performance on high-resource languages, albeit with limited generalization to genuinely low-resource settings. While prior approaches have attempted to enhance cross-lingual transfer through representation alignment or contrastive learning, they remain constrained by the extremely limited availability of parallel data to provide positive supervision in target languages. In this work, we introduce NeighXLM, a neighbor-augmented contrastive pretraining framework that enriches target-language supervision by mining semantic neighbors from unlabeled corpora. Without relying on human annotations or translation systems, NeighXLM exploits intra-language semantic relationships captured during pretraining to construct high-quality positive pairs. The approach is model-agnostic and can be seamlessly integrated into existing multilingual pipelines. Experiments on Swahili demonstrate the effectiveness of NeighXLM in improving cross-lingual retrieval and zero-shot transfer performance."
}