  Frame semantics-based approaches have been widely used in semantic parsing
tasks and have become mainstream. It remains challenging to disambiguate frame
representations evoked by target lexical units under different contexts.
Pre-trained Language Models (PLMs) have been used in semantic parsing and
significantly improve the accuracy of neural parsers. However, the PLMs-based
approaches tend to favor collocated patterns presented in the training data,
leading to inaccurate outcomes. The intuition here is to design a mechanism to
optimally use knowledge captured in semantic frames in conjunction with PLMs to
disambiguate frames. We propose a novel Knowledge-Augmented Frame Semantic
Parsing Architecture (KAF-SPA) to enhance semantic representation by
incorporating accurate frame knowledge into PLMs during frame semantic parsing.
Specifically, a Memory-based Knowledge Extraction Module (MKEM) is devised to
select accurate frame knowledge and construct the continuous templates in the
high dimensional vector space. Moreover, we design a Task-oriented Knowledge
Probing Module (TKPM) using hybrid prompts (in terms of continuous and discrete
prompts) to incorporate the selected knowledge into the PLMs and adapt PLMs to
the tasks of frame and argument identification. Experimental results on two
public FrameNet datasets demonstrate that our method significantly outperforms
strong baselines (by more than +3$\%$ in F1), achieving state-of-art results on
the current benchmark. Ablation studies verify the effectiveness of KAF-SPA.
