  Frame Semantic Role Labeling (FSRL) identifies arguments and labels them with
frame semantic roles defined in FrameNet. Previous researches tend to divide
FSRL into argument identification and role classification. Such methods usually
model role classification as naive multi-class classification and treat
arguments individually, which neglects label semantics and interactions between
arguments and thus hindering performance and generalization of models. In this
paper, we propose a query-based framework named ArGument Extractor with
Definitions in FrameNet (AGED) to mitigate these problems. Definitions of
frames and frame elements (FEs) in FrameNet can be used to query arguments in
text. Encoding text-definition pairs can guide models in learning label
semantics and strengthening argument interactions. Experiments show that AGED
outperforms previous state-of-the-art by up to 1.3 F1-score in two FrameNet
datasets and the generalization power of AGED in zero-shot and fewshot
scenarios. Our code and technical appendix is available at
https://github.com/PKUnlp-icler/AGED.
