Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis

TL;DR

This paper introduces a modernized, efficient structured SRL framework that maintains explicit predicate-argument representations, improves inference speed, and enhances structural stability using dependency cues, with applications to multilingual SRL.

Contribution

It presents a new encoder-based SRL model that is faster, maintains explicit structure, and leverages dependency information for improved stability and multilingual projection.

Findings

Inference speed is increased tenfold.

Dependency cues improve structural stability.

RoBERTa and DeBERTa enhance F1 performance.

Abstract

Semantic Role Labeling (SRL) provides an explicit representation of predicate-argument structure, capturing linguistically grounded relations such as who did what to whom. While recent NLP progress has been dominated by large language models (LLMs), these systems often rely on implicit semantic representations, often lacking explicit structural constraints and systematic explanatory mechanisms. Traditionally, SRL systems have often relied on AllenNLP; however, the framework entered maintenance mode in December 2022, limiting compatibility with evolving encoder architectures and modern inference requirements. We revisit structured SRL modeling, introducing a modernized encoder-based framework that preserves explicit predicate-argument structure while enabling inference 10 times faster. Using BERT-base, the model attains comparable predictive performance, and RoBERTa and DeBERTa further improve F1 performance within the same framework. We adopt a dependency-informed diagnostic methodology to characterize span-level inconsistencies and conduct a representation-level analysis of LLM behavior under dependency-informed structural signals. Results indicate that dependency cues primarily improve structural stability. Finally, we illustrate how the framework's explicit predicate-argument structure can support multilingual SRL projection as a downstream application.