  Abstract Meaning Representation (AMR) parsing aims to translate sentences to
semantic representation with a hierarchical structure, and is recently
empowered by pretrained sequence-to-sequence models. However, there exists a
gap between their flat training objective (i.e., equally treats all output
tokens) and the hierarchical AMR structure, which limits the model
generalization. To bridge this gap, we propose a Hierarchical Curriculum
Learning (HCL) framework with Structure-level (SC) and Instance-level Curricula
(IC). SC switches progressively from core to detail AMR semantic elements while
IC transits from structure-simple to -complex AMR instances during training.
Through these two warming-up processes, HCL reduces the difficulty of learning
complex structures, thus the flat model can better adapt to the AMR hierarchy.
Extensive experiments on AMR2.0, AMR3.0, structure-complex and
out-of-distribution situations verify the effectiveness of HCL.
