Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
This work addresses the need for more interpretable and flexible SRL systems in natural language processing, though it is incremental as it builds on existing SRL and definition modeling techniques.
The paper tackles the problem of Semantic Role Labeling (SRL) by proposing a new formulation that uses natural language definitions instead of discrete labels, aiming to improve interpretability and flexibility. The results show that this approach maintains competitive performance on standard SRL benchmarks without sacrificing accuracy.
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.