  Higher-order features bring significant accuracy gains in semantic dependency
parsing. However, modeling higher-order features with exact inference is
NP-hard. Graph neural networks (GNNs) have been demonstrated to be an effective
tool for solving NP-hard problems with approximate inference in many graph
learning tasks. Inspired by the success of GNNs, we investigate building a
higher-order semantic dependency parser by applying GNNs. Instead of explicitly
extracting higher-order features from intermediate parsing graphs, GNNs
aggregate higher-order information concisely by stacking multiple GNN layers.
Experimental results show that our model outperforms the previous
state-of-the-art parser on the SemEval 2015 Task 18 English datasets.
