Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
Simone Conia, Edoardo Barba, Alessandro Scirè, Roberto Navigli · 2022
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 FrameNetstyle, 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.