When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications

Understand

Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation.

• Many impacting factors, including data quantity, similarity among tasks, and the balance between general language model and task-specific adaptation, can affect the performance of MAML in NLP, but few works have thoroughly studied them.
• In this paper, we conduct an empirical study to investigate these impacting factors and conclude when MAML works the best based on the experimental results.