  Current end-to-end semantic role labeling is mostly accomplished via
graph-based neural models. However, these all are first-order models, where
each decision for detecting any predicate-argument pair is made in isolation
with local features. In this paper, we present a high-order refining mechanism
to perform interaction between all predicate-argument pairs. Based on the
baseline graph model, our high-order refining module learns higher-order
features between all candidate pairs via attention calculation, which are later
used to update the original token representations. After several iterations of
refinement, the underlying token representations can be enriched with globally
interacted features. Our high-order model achieves state-of-the-art results on
Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile
relieving the long-range dependency issues.
