  One of the common traits of past and present approaches for Semantic Role
Labeling (SRL) is that they rely upon discrete labels drawn from a predefined
linguistic inventory to classify predicate senses and their arguments. However,
we argue this need not be the case. In this paper, we present an approach that
leverages Definition Modeling to introduce a generalized formulation of SRL as
the task of describing predicate-argument structures using natural language
definitions instead of discrete labels. Our novel formulation takes a first
step towards placing interpretability and flexibility foremost, and yet our
experiments and analyses on PropBank-style and FrameNet-style, dependency-based
and span-based SRL also demonstrate that a flexible model with an interpretable
output does not necessarily come at the expense of performance. We release our
software for research purposes at https://github.com/SapienzaNLP/dsrl.
