  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.
