Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures

Simone Conia, Edoardo Barba, Alessandro Scirè, Roberto Navigli


Abstract
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.
Anthology ID:
2022.findings-emnlp.313
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4253–4270
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.313
DOI:
10.18653/v1/2022.findings-emnlp.313
Bibkey:
Cite (ACL):
Simone Conia, Edoardo Barba, Alessandro Scirè, and Roberto Navigli. 2022. Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4253–4270, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures (Conia et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.313.pdf