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Cross-lingual transfer (CLT) is of various applications. However, labeledcross-lingual corpus is expensive or even inaccessible, especially in thefields where labels are private, such as diagnostic results of symptoms inmedicine and user profiles in business. Nevertheless, there are off-the-shelfmodels in these sensitive fields. Instead of pursuing the original labels, aworkaround for CLT is to transfer knowledge from the off-the-shelf modelswithout labels. To this end, we define a novel CLT problem named FreeTransfer-Xthat aims to achieve knowledge transfer from the off-the-shelf models inrich-resource languages. To address the problem, we propose a 2-step knowledgedistillation (KD, Hinton et al., 2015) framework based on multilingualpre-trained language models (mPLM). The significant improvement over strongneural machine translation (NMT) baselines demonstrates the effectiveness ofthe proposed method. In addition to reducing annotation cost and protectingprivate labels, the proposed method is compatible with different networks andeasy to be deployed. Finally, a range of analyses indicate the great potentialof the proposed method.

FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models