  The goal of Event Argument Extraction (EAE) is to find the role of each
entity mention for a given event trigger word. It has been shown in the
previous works that the syntactic structures of the sentences are helpful for
the deep learning models for EAE. However, a major problem in such prior works
is that they fail to exploit the semantic structures of the sentences to induce
effective representations for EAE. Consequently, in this work, we propose a
novel model for EAE that exploits both syntactic and semantic structures of the
sentences with the Graph Transformer Networks (GTNs) to learn more effective
sentence structures for EAE. In addition, we introduce a novel inductive bias
based on information bottleneck to improve generalization of the EAE models.
Extensive experiments are performed to demonstrate the benefits of the proposed
model, leading to state-of-the-art performance for EAE on standard datasets.
