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

Problem
This paper addresses the limitations of existing Semantic Role Labeling (SRL) systems, particularly those reliant on the AllenNLP framework, which has entered maintenance mode as of December 2022. The authors highlight the gap in the literature regarding the integration of explicit semantic representations in the context of large language models (LLMs), which often lack structured constraints and systematic explanatory mechanisms. The work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose a modernized encoder-based framework for SRL that maintains explicit predicate-argument structures while significantly enhancing inference speed—reportedly achieving a 10-fold increase in efficiency. The architecture leverages BERT-base as a foundational model, with further enhancements using RoBERTa and DeBERTa to improve F1 scores. The training methodology incorporates a dependency-informed diagnostic approach, which allows for a detailed analysis of span-level inconsistencies and the behavior of LLMs under dependency-informed structural signals. The framework is designed to facilitate multilingual SRL projection, indicating its applicability across diverse linguistic contexts.

Results
The proposed framework demonstrates competitive performance against established baselines. Specifically, the BERT-base model achieves comparable predictive performance to existing SRL systems, while RoBERTa and DeBERTa yield improved F1 scores. Although specific numerical results are not disclosed in the abstract, the authors emphasize that the dependency-informed approach enhances structural stability, suggesting a meaningful effect size in terms of performance metrics. The results indicate that the integration of dependency cues leads to more robust SRL outputs.

Limitations
The authors acknowledge that their framework, while efficient, may still be constrained by the inherent limitations of the underlying transformer architectures, such as BERT, RoBERTa, and DeBERTa. They do not address potential issues related to the scalability of the framework to larger datasets or the generalizability of the dependency-informed methodology across different languages and domains. Additionally, the lack of peer review may imply that the findings should be interpreted with caution until validated by the community.

Why it matters
This work has significant implications for the field of NLP, particularly in the context of SRL, where explicit semantic representations can enhance interpretability and robustness. By providing a framework that integrates efficient structured inference with dependency-informed analysis, the authors pave the way for future research that can leverage these insights for improved multilingual SRL applications. The findings may also influence the design of future LLMs, encouraging the incorporation of explicit structural constraints to enhance semantic understanding.

Authors: Sangpil Youm, Leah Jones, Bonnie J. Dorr
Source: arXiv:2605.02505
URL: https://arxiv.org/abs/2605.02505v1