@inproceedings{nguyen-etal-2025-kda,
    title = "{KDA}: Knowledge Distillation Adapter for Cross-Lingual Transfer",
    author = "Nguyen, Ta-Bao  and
      Phan, Nguyen-Phuong  and
      Le, Tung  and
      Nguyen, Huy Tien",
    editor = "Flek, Lucie  and
      Narayan, Shashi  and
      Phương, L{\^e} Hồng  and
      Pei, Jiahuan",
    booktitle = "Proceedings of the 18th International Natural Language Generation Conference",
    month = oct,
    year = "2025",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-lei-gao-usc/2025.inlg-main.8/",
    pages = "122--133",
    abstract = "State-of-the-art cross-lingual transfer often relies on massive multilingual models, but their prohibitive size and computational cost limit their practicality for low-resource languages. An alternative is to adapt powerful, task-specialized monolingual models, but this presents challenges in bridging the vocabulary and structural gaps between languages. To address this, we propose KDA, a Knowledge Distillation Adapter framework that efficiently adapts a fine-tuned, high-resource monolingual model to a low-resource target language. KDA utilizes knowledge distillation to transfer the source model{'}s task-solving capabilities to the target language in a parameter-efficient manner. In addition, we introduce a novel adapter architecture that integrates source-language token embeddings while learning new positional embeddings, directly mitigating cross-lingual representational mismatches. Our empirical results on zero-shot transfer for Vietnamese Sentiment Analysis demonstrate that KDA significantly outperforms existing methods, offering a new, effective, and computationally efficient pathway for cross-lingual transfer."
}