  Frame semantic parsing is a fundamental NLP task, which consists of three
subtasks: frame identification, argument identification and role
classification. Most previous studies tend to neglect relations between
different subtasks and arguments and pay little attention to ontological frame
knowledge defined in FrameNet. In this paper, we propose a Knowledge-guided
Incremental semantic parser with Double-graph (KID). We first introduce Frame
Knowledge Graph (FKG), a heterogeneous graph containing both frames and FEs
(Frame Elements) built on the frame knowledge so that we can derive
knowledge-enhanced representations for frames and FEs. Besides, we propose
Frame Semantic Graph (FSG) to represent frame semantic structures extracted
from the text with graph structures. In this way, we can transform frame
semantic parsing into an incremental graph construction problem to strengthen
interactions between subtasks and relations between arguments. Our experiments
show that KID outperforms the previous state-of-the-art method by up to 1.7
F1-score on two FrameNet datasets. Our code is availavle at
https://github.com/PKUnlp-icler/KID.
