  Multilingual models, such as M-BERT and XLM-R, have gained increasing
popularity, due to their zero-shot cross-lingual transfer learning
capabilities. However, their generalization ability is still inconsistent for
typologically diverse languages and across different benchmarks. Recently,
meta-learning has garnered attention as a promising technique for enhancing
transfer learning under low-resource scenarios: particularly for cross-lingual
transfer in Natural Language Understanding (NLU). In this work, we propose
X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for
NLU. Our approach adapts MAML, an optimization-based meta-learning approach, to
learn to adapt to new languages. We extensively evaluate our framework on two
challenging cross-lingual NLU tasks: multilingual task-oriented dialog and
typologically diverse question answering. We show that our approach outperforms
naive fine-tuning, reaching competitive performance on both tasks for most
languages. Our analysis reveals that X-METRA-ADA can leverage limited data for
faster adaptation.
