A Systematic Survey of Semantic Role Labeling in the Era of Pretrained Language Models

TL;DR

This survey critically analyzes semantic role labeling (SRL) research, introducing a unified taxonomy, examining the role of syntax and large language models, and extending coverage to multimodal settings and evaluation practices.

Contribution

It presents a comprehensive taxonomy of SRL research, analyzes the impact of syntax and large language models, and extends the scope to multimodal SRL and evaluation methods.

Findings

Syntax features provide consistent gains under certain conditions.

Large language models complement specialized SRL systems.

Multimodal SRL extends traditional approaches to visual, video, and speech modalities.

Abstract

Semantic role labeling (SRL) is a central natural language processing task for understanding predicate-argument structures within texts and enabling downstream applications. Despite extensive research, comprehensive surveys that critically synthesize the field from a unified perspective remain lacking. This survey makes several contributions beyond organizing existing work. We propose a unified four-dimensional taxonomy that categorizes SRL research along model architectures, syntax feature modeling, application scenarios, and multimodal extensions. We provide a critical analysis of when and why syntactic features help, identifying conditions under which syntax-aided approaches provide consistent gains over syntax-free counterparts. We offer the first systematic treatment of SRL in the era of large language models, examining the complementary roles of LLMs and specialized SRL systems and identifying directions for hybrid approaches. We extend the scope of SRL surveys to cover multimodal settings including visual, video, and speech modalities, and analyze structural differences in evaluation across these modalities. Literature was collected through systematic searches of the ACL Anthology, IEEE Xplore, the ACM Digital Library, and Google Scholar, covering publications from 2000 to 2025 and applying explicit inclusion and exclusion criteria to yield approximately 200 primary references. SRL benchmarks, evaluation metrics, and paradigm modeling approaches are discussed alongside practical applications across domains. Future research directions are analyzed, addressing the evolving role of SRL with large language models and broader NLP impact.