@inproceedings{chi-etal-2023-cross,
    title = "Can Cross-Lingual Transferability of Multilingual Transformers Be Activated Without End-Task Data?",
    author = "Chi, Zewen  and
      Huang, Heyan  and
      Mao, Xian-Ling",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.findings-acl.796/",
    doi = "10.18653/v1/2023.findings-acl.796",
    pages = "12572--12584",
    abstract = "Pretrained multilingual Transformers have achieved great success in cross-lingual transfer learning. Current methods typically activate the cross-lingual transferability of multilingual Transformers by fine-tuning them on end-task data. However, the methods cannot perform cross-lingual transfer when end-task data are unavailable. In this work, we explore whether the cross-lingual transferability can be activated without end-task data. We propose a cross-lingual transfer method, named PlugIn-X. PlugIn-X disassembles monolingual and multilingual Transformers into sub-modules, and reassembles them to be the multilingual end-task model. After representation adaptation, PlugIn-X finally performs cross-lingual transfer in a plug-and-play style. Experimental results show that PlugIn-X successfully activates the cross-lingual transferability of multilingual Transformers without accessing end-task data. Moreover, we analyze how the cross-model representation alignment affects the cross-lingual transferability."
}