Curriculum meta-learning for zero-shot cross-lingual transfer

Learning with small data is the challenge facing low-resource domains with insufficient data to perform the target tasks. Among the state-of-the-art approaches in deep learning and transfer learning, the approach based on meta-learning stands out for its ability to balance the bias from high-resource to the low-resource domains. To crack the aforementioned problem, we proposed a framework utilizing meta-learning with zero- shot cross-lingual natural language inferences as a use case. Additionally, to explore how the sensitivity of the meta-gradient impacts learning performance, we applied two curriculum strategies: cross-review evaluation and annealing arrangement. The experiments confirm the outperforming of our framework on a benchmark dataset.