  Multilingual pre-trained contextual embedding models (Devlin et al., 2019)
have achieved impressive performance on zero-shot cross-lingual transfer tasks.
Finding the most effective fine-tuning strategy to fine-tune these models on
high-resource languages so that it transfers well to the zero-shot languages is
a non-trivial task. In this paper, we propose a novel meta-optimizer to
soft-select which layers of the pre-trained model to freeze during fine-tuning.
We train the meta-optimizer by simulating the zero-shot transfer scenario.
Results on cross-lingual natural language inference show that our approach
improves over the simple fine-tuning baseline and X-MAML (Nooralahzadeh et al.,
2020).
