  We study the problem of integrating syntactic information from constituency
trees into a neural model in Frame-semantic parsing sub-tasks, namely Target
Identification (TI), FrameIdentification (FI), and Semantic Role Labeling
(SRL). We use a Graph Convolutional Network to learn specific representations
of constituents, such that each constituent is profiled as the production
grammar rule it corresponds to. We leverage these representations to build
syntactic features for each word in a sentence, computed as the sum of all the
constituents on the path between a word and a task-specific node in the tree,
e.g. the target predicate for SRL. Our approach improves state-of-the-art
results on the TI and SRL of ~1%and~3.5% points, respectively (+2.5% additional
points are gained with BERT as input), when tested on FrameNet 1.5, while
yielding comparable results on the CoNLL05 dataset to other syntax-aware
systems.
