  Frame semantic parsing is a semantic analysis task based on FrameNet which
has received great attention recently. The task usually involves three subtasks
sequentially: (1) target identification, (2) frame classification and (3)
semantic role labeling. The three subtasks are closely related while previous
studies model them individually, which ignores their intern connections and
meanwhile induces error propagation problem. In this work, we propose an
end-to-end neural model to tackle the task jointly. Concretely, we exploit a
graph-based method, regarding frame semantic parsing as a graph construction
problem. All predicates and roles are treated as graph nodes, and their
relations are taken as graph edges. Experiment results on two benchmark
datasets of frame semantic parsing show that our method is highly competitive,
resulting in better performance than pipeline models.
