To address the issues, we propose meta graph learning (MGL), a meta learning framework to learn how to cross-lingual transfer for mPLM.
mGL models each CLT process as heterogeneous information propagation over a dynamic graph, which captures latent language correlations and makes the downstream CLT adaptation more interpretable.