  Frame semantic parsing is a complex problem which includes multiple
underlying subtasks. Recent approaches have employed joint learning of subtasks
(such as predicate and argument detection), and multi-task learning of related
tasks (such as syntactic and semantic parsing). In this paper, we explore
multi-task learning of all subtasks with transformer-based models. We show that
a purely generative encoder-decoder architecture handily beats the previous
state of the art in FrameNet 1.7 parsing, and that a mixed decoding multi-task
approach achieves even better performance. Finally, we show that the multi-task
model also outperforms recent state of the art systems for PropBank SRL parsing
on the CoNLL 2012 benchmark.
