New survey details semantic role labeling in the era of LLMs

A new survey paper provides a comprehensive overview of semantic role labeling (SRL) within the context of modern pretrained language models. The paper introduces a four-dimensional taxonomy to categorize SRL research, focusing on model architectures, syntax feature modeling, application scenarios, and multimodal extensions. It critically analyzes the utility of syntactic features and explores the synergistic relationship between large language models and specialized SRL systems, proposing directions for hybrid approaches. The survey also extends its scope to multimodal SRL, including visual, video, and speech data, and discusses evaluation metrics and future research trajectories. AI

IMPACT Provides a structured overview of SRL advancements, aiding researchers in understanding LLM integration and multimodal applications.

RANK_REASON The cluster contains a research paper published on arXiv detailing a survey of a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Huiyao Chen
• Large Language Models
• Pretrained Language Models
• Semantic Role Labeling

• paper
• other

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