  Event argument extraction (EAE) identifies event arguments and their specific
roles for a given event. Recent advancement in generation-based EAE models has
shown great performance and generalizability over classification-based models.
However, existing generation-based EAE models mostly focus on problem
re-formulation and prompt design, without incorporating additional information
that has been shown to be effective for classification-based models, such as
the abstract meaning representation (AMR) of the input passages. Incorporating
such information into generation-based models is challenging due to the
heterogeneous nature of the natural language form prevalently used in
generation-based models and the structured form of AMRs. In this work, we study
strategies to incorporate AMR into generation-based EAE models. We propose
AMPERE, which generates AMR-aware prefixes for every layer of the generation
model. Thus, the prefix introduces AMR information to the generation-based EAE
model and then improves the generation. We also introduce an adjusted copy
mechanism to AMPERE to help overcome potential noises brought by the AMR graph.
Comprehensive experiments and analyses on ACE2005 and ERE datasets show that
AMPERE can get 4% - 10% absolute F1 score improvements with reduced training
data and it is in general powerful across different training sizes.
