Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis
This work provides an efficient, explicit SRL framework for NLP practitioners needing structured semantic representations, though it is an incremental improvement over existing methods.
The authors present a modernized encoder-based Semantic Role Labeling framework that achieves 10x faster inference while maintaining comparable performance to prior systems, with F1 improvements using RoBERTa and DeBERTa. They also demonstrate that dependency cues improve structural stability and support multilingual SRL projection.
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.